the temporal variability of river stages (Seibert and Vis, 2016). Similarly, lake volumetric changes can be estimated from stages if the relationship between lake volume and stage height is available (e.g. from accurate DEMs). For bathymetry of rivers and lakes, synthetic aperture radar satellite RS can be used, but it is limited to large (>100 m width) water bodies (Bandini et al., 2020; Dettmering et al., 2020). For the bathymetry of smaller streams, considering space-borne data, only ICESat-2 is currently suitable (Bandini et al., 2023; Liu et al., 2022). If in a given project location, ICESat-2 data is not available, targeted data collection requests to the National Snow and Ice Data Center (NSIDC) are sometimes considered, although not guaranteed (information from personal communication with NSIDC). If no relevant satellite data is available, airborne or drone-borne bathymetry applications can be considered using green LiDAR or GPR sensors.

## 5.4. Observations to constrain calibrated state variables

Observations are indispensable to constrain state variables in the calibration or data assimilation of a model. The reliability of IHMs depends on the number of observations, preferably of different types, but also on data quality and its spatiotemporal distribution. Schilling et al. (2019) argue that classical observations (heads and stream flows) do not contain sufficient information and that additional, unconventional observations are needed to calibrate models reliably. In that respect, we emphasize the benefits of RS and hydrogeophysics as additional unconventional data sources for calibration and data assimilation because they provide spatially semi-continuous (gridded) data with good temporal resolution. Such data is usually not as accurate as the in-situ measured equivalents, but still can contribute to reducing model uncertainty, particularly because the current modelling technology can handle computer-power-demanding, spatially semi-continuous observations and also allow for multivariate calibration and data assimilation (Khaki et al., 2020), where the confidence of data can be assigned through weights (White et al., 2020).

The observation of surface water body stages is one of the most important RS and hydrogeophysical applications. Certainly, the simplest and the most accurate way of monitoring water levels, so also stages, is by installing cost-effective pressure transducers. However, their use in hydrological monitoring is not always possible, e.g., in cases when sites are not accessible or risky for a person installing it or for the device to be stolen, vandalised or taken away by floods. For monitoring of stages of large surface water bodies (>100 m), high-resolution altimetry products such as Jason-2/3, Cryosat-2 or Sentinel 3A/3B missions (Deidda et al., 2021) or the recently launched SWOT (Biancamaria et al., 2016) are suitable.

Monitoring of stages of small-sized surface water bodies (<100 m) can be done indirectly by very high-resolution optical images with spatial resolution ~ 1 m (such space-borne images are proprietary), provided river or lake relation between a surface water body extent and a stage is available. In such a case, a stage is deduced from the spatiotemporal analysis of the surface water body extent. For that task, the river width software package called RivWidthCloud, developed on the Google Earth Engine cloud computing platform, is particularly suitable as it automatically extracts river centerlines and widths from optical images (Yang et al., 2020). Stages of small-sized surface water bodies can also be accurately monitored by space-borne ICESat-2 and by airborne or drone-borne green LiDAR or GPR.

Evapotranspiration is probably the most widely used RS observation type applied as a constraint of state variables in IHM calibration and data assimilation. In-situ ET measurements, e.g. through eddy covariance towers, require expensive equipment and specialised processing expertise and are restricted to small footprints. As such, the widespread use of eddy covariance towers to provide spatiotemporally distributed ET estimates for a catchment, is unrealistic. Thermal RS energy balance approaches provide cost-effective alternatives for the ET assessment at the catchment, regional or global scales, and they integrate well with

IHMs. However, the processing of such data to obtain ET is complex and specialised. A review of different RS-based ET estimation algorithms is presented by Zhang et al. (2016). However, RS ET is also readily available (via the Internet) for non-experts as RS ET products. The most widely used in IHMs is the MODIS ET product, available at 500 m spatial and eight days temporal resolution. Its use has already been proven beneficial for lowering the predictive uncertainty of IHMs (Gelsinari et al., 2020; Gelsinari et al., 2022; Gelsinari et al., 2021). If an IHM needs ET of higher spatial resolution, the following options can be considered: i) downscaling the MODIS ET, which is the simplest but also the least accurate option (does not require specialised RS ET expertise); ii) use of higher resolution RS data, e.g. Landsat data processed through Google Earth Engine (Senay et al., 2022) and RS energy balance algorithms (requires RS ET expertise); iii) use of UAVs lightweight sensors, such as multispectral cameras, including thermal infrared spectrum (requires not only RS ET expertise but also UAV expertise); such UAVs can provide ET at a spatial resolution in the order of centimeters (Niu et al., 2020).

Another RS observation type applied as a constraint in IHM calibration and data assimilation is soil moisture (SM). SM products are generally uncertain (Peng et al., 2017), mainly because images applied to determine SM are too coarse to depict small-scale SM patterns. For example, according to Peng et al. (2017), global SM products (e.g. SMAP ~ 36 km or ASCAT ~ 25 km) are not suitable for regional studies due to their coarse spatial resolution. The new, high-resolution SM product from Sentinel-1 (C-band), at 1 km spatial and daily temporal resolution, is promising for the use as a state variable of IHMs (Xu, 2021), but is mainly restricted to Europe. Moreover, the number of usable Sentinel-1 images depends strongly on the area chosen. In 2024, the even higher spatial resolution SM NISAR (S- and L-band) product is expected (https://nisar.jpl.nasa.gov/mission/quick-facts/), with 3-10 m mode-dependent spatial resolution and 12 days repeat cycle. Such high-resolution SM data may contribute substantially to lowering IHM uncertainty, although this still has to be proven. Very high-resolution soil moisture can also be acquired from airborne and drone-borne hydrogeophysics.

The total water storage (TWS), especially its change in time obtained from gravity surveys, can be a very useful input for calibration or data assimilation of IHMs. TWS can be obtained from satellite gravity missions (e.g. GRACE), airborne gravity missions and ground gravity surveys with gravimeters. The GRACE mission provides excellent data for global and continental research projects, and its TWS changes have been widely validated, and methods to separate contributions of different hydrological domains have been developed (Li et al., 2019; Shen et al., 2015). However, the extremely coarse spatial resolution of GRACE data, limits its use for applied management projects at the regional, catchment or finer scale. Airborne gravity surveys are quite common in earth resources surveys (oil, gas, etc.) but not in hydrology. This is mainly because of technical implementation barriers related to low signal-to-noise ratio of airborne gravity implementation due to the large aircraft noise. The ground-based change of TWS by gravimeters applied as input for local scale models has already been tested in a simple conceptual model (not IHM) by Creutzfeldt et al. (2010) and in a MODFLOW96 groundwater model by Christiansen et al., (2011a). However, gravimeters: i) are limited in use due to demanding site installation (not mobile); ii) provide changes in total water storage without discriminating between different components spatially; iii) are too expensive to be widely applied in water management projects, as there are more efficient and cheaper ways to characterize changes in subsurface water storage, for example, by installing pressure transducers. Therefore, despite being interesting from a scientific point of view, the ground-based gravimetric measurements of TWS change are not likely to contribute much to IHMs.

RS and hydrogeophysics efficiently provide a lot of valuable data from space, near-surface and ground surface. However, the position of the water table, the most important hydrogeological data type, is still difficult to detect by RS or hydrogeophysical methods with sufficient
the temporal variability of river stages (Seibert and Vis, 2016). Similarly, lake volumetric changes can be estimated from stages if the relationship between lake volume and stage height is available (e.g. from accurate DEMs). For bathymetry of rivers and lakes, synthetic aperture radar satellite RS can be used, but it is limited to large (>100 m width) water bodies (Bandini et al., 2020; Dettmering et al., 2020). For the bathymetry of smaller streams, considering space-borne data, only ICESat-2 is currently suitable (Bandini et al., 2023; Liu et al., 2022). If in a given project location, ICESat-2 data is not available, targeted data collection requests to the National Snow and Ice Data Center (NSIDC) are sometimes considered, although not guaranteed (information from personal communication with NSIDC). If no relevant satellite data is available, airborne or drone-borne bathymetry applications can be considered using green LiDAR or GPR sensors.

## 5.4. Observations to constrain calibrated state variables

Observations are indispensable to constrain state variables in the calibration or data assimilation of a model. The reliability of IHMs depends on the number of observations, preferably of different types, but also on data quality and its spatiotemporal distribution. Schilling et al. (2019) argue that classical observations (heads and stream flows) do not contain sufficient information and that additional, unconventional observations are needed to calibrate models reliably. In that respect, we emphasize the benefits of RS and hydrogeophysics as additional unconventional data sources for calibration and data assimilation because they provide spatially semi-continuous (gridded) data with good temporal resolution. Such data is usually not as accurate as the in-situ measured equivalents, but still can contribute to reducing model uncertainty, particularly because the current modelling technology can handle computer-power-demanding, spatially semi-continuous observations and also allow for multivariate calibration and data assimilation (Khaki et al., 2020), where the confidence of data can be assigned through weights (White et al., 2020).

The observation of surface water body stages is one of the most important RS and hydrogeophysical applications. Certainly, the simplest and the most accurate way of monitoring water levels, so also stages, is by installing cost-effective pressure transducers. However, their use in hydrological monitoring is not always possible, e.g., in cases when sites are not accessible or risky for a person installing it or for the device to be stolen, vandalised or taken away by floods. For monitoring of stages of large surface water bodies (>100 m), high-resolution altimetry products such as Jason-2/3, Cryosat-2 or Sentinel 3A/3B missions (Deidda et al., 2021) or the recently launched SWOT (Biancamaria et al., 2016) are suitable.

Monitoring of stages of small-sized surface water bodies (<100 m) can be done indirectly by very high-resolution optical images with spatial resolution ~ 1 m (such space-borne images are proprietary), provided river or lake relation between a surface water body extent and a stage is available. In such a case, a stage is deduced from the spatiotemporal analysis of the surface water body extent. For that task, the river width software package called RivWidthCloud, developed on the Google Earth Engine cloud computing platform, is particularly suitable as it automatically extracts river centerlines and widths from optical images (Yang et al., 2020). Stages of small-sized surface water bodies can also be accurately monitored by space-borne ICESat-2 and by airborne or drone-borne green LiDAR or GPR.

Evapotranspiration is probably the most widely used RS observation type applied as a constraint of state variables in IHM calibration and data assimilation. In-situ ET measurements, e.g. through eddy covariance towers, require expensive equipment and specialised processing expertise and are restricted to small footprints. As such, the widespread use of eddy covariance towers to provide spatiotemporally distributed ET estimates for a catchment, is unrealistic. Thermal RS energy balance approaches provide cost-effective alternatives for the ET assessment at the catchment, regional or global scales, and they integrate well with

IHMs. However, the processing of such data to obtain ET is complex and specialised. A review of different RS-based ET estimation algorithms is presented by Zhang et al. (2016). However, RS ET is also readily available (via the Internet) for non-experts as RS ET products. The most widely used in IHMs is the MODIS ET product, available at 500 m spatial and eight days temporal resolution. Its use has already been proven beneficial for lowering the predictive uncertainty of IHMs (Gelsinari et al., 2020; Gelsinari et al., 2022; Gelsinari et al., 2021). If an IHM needs ET of higher spatial resolution, the following options can be considered: i) downscaling the MODIS ET, which is the simplest but also the least accurate option (does not require specialised RS ET expertise); ii) use of higher resolution RS data, e.g. Landsat data processed through Google Earth Engine (Senay et al., 2022) and RS energy balance algorithms (requires RS ET expertise); iii) use of UAVs lightweight sensors, such as multispectral cameras, including thermal infrared spectrum (requires not only RS ET expertise but also UAV expertise); such UAVs can provide ET at a spatial resolution in the order of centimeters (Niu et al., 2020).

Another RS observation type applied as a constraint in IHM calibration and data assimilation is soil moisture (SM). SM products are generally uncertain (Peng et al., 2017), mainly because images applied to determine SM are too coarse to depict small-scale SM patterns. For example, according to Peng et al. (2017), global SM products (e.g. SMAP ~ 36 km or ASCAT ~ 25 km) are not suitable for regional studies due to their coarse spatial resolution. The new, high-resolution SM product from Sentinel-1 (C-band), at 1 km spatial and daily temporal resolution, is promising for the use as a state variable of IHMs (Xu, 2021), but is mainly restricted to Europe. Moreover, the number of usable Sentinel-1 images depends strongly on the area chosen. In 2024, the even higher spatial resolution SM NISAR (S- and L-band) product is expected (https://nisar.jpl.nasa.gov/mission/quick-facts/), with 3-10 m mode-dependent spatial resolution and 12 days repeat cycle. Such high-resolution SM data may contribute substantially to lowering IHM uncertainty, although this still has to be proven. Very high-resolution soil moisture can also be acquired from airborne and drone-borne hydrogeophysics.

The total water storage (TWS), especially its change in time obtained from gravity surveys, can be a very useful input for calibration or data assimilation of IHMs. TWS can be obtained from satellite gravity missions (e.g. GRACE), airborne gravity missions and ground gravity surveys with gravimeters. The GRACE mission provides excellent data for global and continental research projects, and its TWS changes have been widely validated, and methods to separate contributions of different hydrological domains have been developed (Li et al., 2019; Shen et al., 2015). However, the extremely coarse spatial resolution of GRACE data, limits its use for applied management projects at the regional, catchment or finer scale. Airborne gravity surveys are quite common in earth resources surveys (oil, gas, etc.) but not in hydrology. This is mainly because of technical implementation barriers related to low signal-to-noise ratio of airborne gravity implementation due to the large aircraft noise. The ground-based change of TWS by gravimeters applied as input for local scale models has already been tested in a simple conceptual model (not IHM) by Creutzfeldt et al. (2010) and in a MODFLOW96 groundwater model by Christiansen et al., (2011a). However, gravimeters: i) are limited in use due to demanding site installation (not mobile); ii) provide changes in total water storage without discriminating between different components spatially; iii) are too expensive to be widely applied in water management projects, as there are more efficient and cheaper ways to characterize changes in subsurface water storage, for example, by installing pressure transducers. Therefore, despite being interesting from a scientific point of view, the ground-based gravimetric measurements of TWS change are not likely to contribute much to IHMs.

RS and hydrogeophysics efficiently provide a lot of valuable data from space, near-surface and ground surface. However, the position of the water table, the most important hydrogeological data type, is still difficult to detect by RS or hydrogeophysical methods with sufficient
accuracy. Due to low penetration depth, RS does not contribute at all. Also, hydrogeophysics has little to offer for assessing water table depth. The best in that respect is MRS, which can detect water table down to 100–150 m depth, but with an accuracy not better than +/-1m for the first ~ 50 m depth b.g.s., and declining with depth. GPR detection of the water table is unreliable as it works in some areas, while in others, even in favourable resistive environments, it does not (Mahmoudzadeh et al., 2012). The GPR method can be affected by various survey-disturbing factors such as the number of reflective subsurface layers, the gradational transition from the unsaturated to the saturated zone due to a capillary fringe, heterogeneity, electrical conductivity, etc. Promising for water table depth estimation are the research developments in using seismic noise (see p. 4.2), but to our knowledge, there is no operational, non-invasive hydrogeophysical method yet that can reliably and accurately (at least up to a few centimetres accuracy) estimate and monitor water table depths. As such, the assessment of hydraulic heads is still bound to in-situ measurements and precise surveys of well elevation (see section 3).

## 5.5. Scales and uncertainties

The scale of a model, especially while using IHMs, is an important constraint because, together with the resolution of models, the scale determines input data requirements and also reliability of model simulations. However, quantitative definitions and terminology related to scales in hydrology are not well defined. If global and continental scales are unique, the two potentially overlapping scale categories, i.e. regional vs catchment vs basin scales and local vs plot vs hillslope scales, are not well defined and not quantified. Global and continental scale ‘research IHMs’ typically have grids coarser than 5 km in contrast to hyperresolution models with grid resolution of 1 km or finer (Beven et al., 2015). The global and the continental ‘research IHMs’ are typically not calibrated due to their size, complexity and computational effort. Instead, they are just evaluated with available data (in-situ, RS and hydrogeophysics). These models (published in large numbers in high-cited journals) are considered to be helping to understand and predict water cycle changes over large scales and the spatial distribution of land-atmosphere moisture and energy fluxes (Maxwell et al., 2015), including their spatiotemporal variability (Schwingshackl et al., 2017) and also to capture macro-scale processes, which can affect water security (Naz et al., 2023). However, according to Bronstert et al. (2017), such models “*can be of little or no use for water resources decisions and even misleading for public debates or decision making*” because “*many hydrological processes are of non-linear nature, including threshold-type behaviour*”, and they “*cannot be reflected by such large scale entities*”. A similar opinion is presented by Beven et al. (2015). Nevertheless, there is the vision that one day, it may be feasible to perform global-scale hydrological modelling with an acceptable accuracy at a much higher resolution than today, i.e. with grid cells of 1 km to 100 m (Döll et al., 2016; Wood et al., 2011) or even finer. However, before that happens, more ‘applied IHMs’ at the regional/catchment/basin, with resolution closer to 100 m rather than to 1000 m, should be developed and calibrated.

Nowadays, earth observations from satellites, aircraft and ground surfaces provide data of various types at different spatiotemporal resolutions and with different uncertainties, influencing the predictive uncertainty of IHMs. Current modelling techniques can quantify uncertainties but there are also ways to reduce model uncertainty: i) when using RS or hydrogeophysics data as input of IHM, always try to improve the reliability of the input data by removing bias with available ground data, as there is a risk of error propagation to the final IHM results; ii) when calibrating a model, try to use automated calibration (optimisation) (Schilling et al., 2019); however if shortage of computer power makes such calibration unrealistic, apply forward calibration (by trial and error) and once obtaining satisfactory results, apply final automated optimisation; iii) quantify model uncertainty; automated optimisation codes typically provide uncertainty estimates; iv) use as

many observations and as many different observation types as possible to constrain state variables of all parts of the model domain (Finger et al., 2015), as it is challenging to improve an IHM, having data of only one part of the model domain (Camporese and Girotto, 2022), e.g. only of shallow soil moisture; v) when possible, apply data assimilation, with joint update of system states and model parameters, which usually is more effective in the reduction of model uncertainty than standard model calibration (Camporese and Girotto, 2022); e.g. the weighted multivariate objective functions, implemented in PEST++ (White et al., 2020), allow simultaneous calibration and data assimilation against diverse observation types using automated mathematical calibration routines; however, that approach is complex, especially when dealing with RS data (e.g. ET or SM), which cover the entire grid, so considerable computer power is required, typically not available in standard PC machines.

IHMs provide unquestionable benefits to hydrology and water management, but they are also computer power demanding, especially the 3D, fully-coupled IHMs, which: i) involve highly non-linear solutions of Richards’ equation; ii) use different types of observations, particularly spatially-semi-continuous (gridded) observations, constraining entire grid domains, as e.g. RS-based ET; iii) use automated optimisation or data assimilation packages. However, nowadays, the availability of computational resources is widely increasing, especially in research organisations, so computer resources can be provided in different forms, for example, through: (a) high-performance clusters, which may offer hundreds of cores and multiple CPUs, i.e. Linux servers; (b) multiple computers connected through an office network; and (c) cloud computing platforms. The main difference between them is that (a) and (b) are local options while (c) is cloud-based. Some cloud computing platforms, such as Google Earth Engine, are available worldwide. In contrast, others are available only for specific applications or in certain areas, such as the SURF platform in the Netherlands (https://www.surf.nl/en/research-it).

## 6. Conclusions and future perspectives

The advent of IHMs has opened up exciting prospects for the broader use of remote sensing and hydrogeophysics as input data for integrated water resources modelling and management with IHMs.

The dynamic integration of all hydrological domains in IHMs implies that IHMs are more reliable than standalone models, especially in areas with distinct surface-groundwater interactions, but also more data- and computer-power demanding.

While coarse-scale ‘research’ IHMs (e.g. at global or continental scale) continue to improve, the ‘applied’, finer spatial resolution IHMs ($\le 1$ km$^2$), still face many challenges; they struggle to close the water balance despite being the most needed for integrated water resources management at the local, catchment and regional scales.

A significant advantage of RS and hydrogeophysics as input data of IHMs is that they can provide semi-continuous spatial data coverage of model domains. Additionally, RS provides a spatiotemporal data archive, while hydrogeophysics can provide spatiotemporal data on demand with the desired spatial and temporal resolution. The main disadvantage of RS and hydrogeophysical data is their uncertainty, which differs between the methods and applications, although reference in-situ measurements can substantially reduce that uncertainty.

Most spatial observations acquired with satellite RS can also be acquired through airborne or drone-borne hydrogeophysics. However, not every surface hydrogeophysical method can be implemented through airborne or drone-borne surveys (e.g. MRS cannot). Thanks to recent drone and sensors’ developments, drone-borne applications already cover most airborne applications. However, they still have limits, mainly due to restricted payloads and survey difficulties in extreme conditions.

IHMs use four types of data: i) climate forcings; ii) parameters; iii) boundary conditions, and iv) observations to constrain model state variables.
accuracy. Due to low penetration depth, RS does not contribute at all. Also, hydrogeophysics has little to offer for assessing water table depth. The best in that respect is MRS, which can detect water table down to 100–150 m depth, but with an accuracy not better than +/-1m for the first ~ 50 m depth b.g.s., and declining with depth. GPR detection of the water table is unreliable as it works in some areas, while in others, even in favourable resistive environments, it does not (Mahmoudzadeh et al., 2012). The GPR method can be affected by various survey-disturbing factors such as the number of reflective subsurface layers, the grada- tional transition from the unsaturated to the saturated zone due to a capillary fringe, heterogeneity, electrical conductivity, etc. Promising for water table depth estimation are the research developments in using seismic noise (see p. 4.2), but to our knowledge, there is no operational, non-invasive hydrogeophysical method yet that can reliably and accu- rately (at least up to a few centimetres accuracy) estimate and monitor water table depths. As such, the assessment of hydraulic heads is still bound to in-situ measurements and precise surveys of well elevation (see section 3).

## 5.5. Scales and uncertainties

The scale of a model, especially while using IHMs, is an important constraint because, together with the resolution of models, the scale determines input data requirements and also reliability of model simu- lations. However, quantitative definitions and terminology related to scales in hydrology are not well defined. If global and continental scales are unique, the two potentially overlapping scale categories, i.e. regional vs catchment vs basin scales and local vs plot vs hillslope scales, are not well defined and not quantified. Global and continental scale ‘research IHMs’ typically have grids coarser than 5 km in contrast to hyperresolution models with grid resolution of 1 km or finer (Beven et al., 2015). The global and the continental ‘research IHMs’ are typi- cally not calibrated due to their size, complexity and computational effort. Instead, they are just evaluated with available data (in-situ, RS and hydrogeophysics). These models (published in large numbers in high-cited journals) are considered to be helping to understand and predict water cycle changes over large scales and the spatial distribution of land-atmosphere moisture and energy fluxes (Maxwell et al., 2015), including their spatiotemporal variability (Schwingshackl et al., 2017) and also to capture macro-scale processes, which can affect water se- curity (Naz et al., 2023). However, according to Bronstert et al. (2017), such models “*can be of little or no use for water resources decisions and even misleading for public debates or decision making*” because “*many hydro- logical processes are of non-linear nature, including threshold-type behav- iour*”, and they “*cannot be reflected by such large scale entities*”. A similar opinion is presented by Beven et al. (2015). Nevertheless, there is the vision that one day, it may be feasible to perform global-scale hydro- logical modelling with an acceptable accuracy at a much higher reso- lution than today, i.e. with grid cells of 1 km to 100 m (Döll et al., 2016; Wood et al., 2011) or even finer. However, before that happens, more ‘applied IHMs’ at the regional/catchment/basin, with resolution closer to 100 m rather than to 1000 m, should be developed and calibrated.

Nowadays, earth observations from satellites, aircraft and ground surfaces provide data of various types at different spatiotemporal reso- lutions and with different uncertainties, influencing the predictive un- certainty of IHMs. Current modelling techniques can quantify uncertainties but there are also ways to reduce model uncertainty: i) when using RS or hydrogeophysics data as input of IHM, always try to improve the reliability of the input data by removing bias with available ground data, as there is a risk of error propagation to the final IHM re- sults; ii) when calibrating a model, try to use automated calibration (optimisation) (Schilling et al., 2019); however if shortage of computer power makes such calibration unrealistic, apply forward calibration (by trial and error) and once obtaining satisfactory results, apply final automated optimisation; iii) quantify model uncertainty; automated optimisation codes typically provide uncertainty estimates; iv) use as

many observations and as many different observation types as possible to constrain state variables of all parts of the model domain (Finger et al., 2015), as it is challenging to improve an IHM, having data of only one part of the model domain (Camporese and Girotto, 2022), e.g. only of shallow soil moisture; v) when possible, apply data assimilation, with joint update of system states and model parameters, which usually is more effective in the reduction of model uncertainty than standard model calibration (Camporese and Girotto, 2022); e.g. the weighted multivariate objective functions, implemented in PEST++ (White et al., 2020), allow simultaneous calibration and data assimilation against diverse observation types using automated mathematical calibration routines; however, that approach is complex, especially when dealing with RS data (e.g. ET or SM), which cover the entire grid, so considerable computer power is required, typically not available in standard PC machines.

IHMs provide unquestionable benefits to hydrology and water management, but they are also computer power demanding, especially the 3D, fully-coupled IHMs, which: i) involve highly non-linear solutions of Richards’ equation; ii) use different types of observations, particularly spatially-semi-continuous (gridded) observations, constraining entire grid domains, as e.g. RS-based ET; iii) use automated optimisation or data assimilation packages. However, nowadays, the availability of computational resources is widely increasing, especially in research organisations, so computer resources can be provided in different forms, for example, through: (a) high-performance clusters, which may offer hundreds of cores and multiple CPUs, i.e. Linux servers; (b) multiple computers connected through an office network; and (c) cloud computing platforms. The main difference between them is that (a) and (b) are local options while (c) is cloud-based. Some cloud computing platforms, such as Google Earth Engine, are available worldwide. In contrast, others are available only for specific applications or in certain areas, such as the SURF platform in the Netherlands (https://www.surf.nl/en/research-it).

## 6. Conclusions and future perspectives

The advent of IHMs has opened up exciting prospects for the broader use of remote sensing and hydrogeophysics as input data for integrated water resources modelling and management with IHMs.

The dynamic integration of all hydrological domains in IHMs implies that IHMs are more reliable than standalone models, especially in areas with distinct surface-groundwater interactions, but also more data- and computer-power demanding.

While coarse-scale ‘research’ IHMs (e.g. at global or continental scale) continue to improve, the ‘applied’, finer spatial resolution IHMs ($\le 1$ km$^2$), still face many challenges; they struggle to close the water balance despite being the most needed for integrated water resources management at the local, catchment and regional scales.

A significant advantage of RS and hydrogeophysics as input data of IHMs is that they can provide semi-continuous spatial data coverage of model domains. Additionally, RS provides a spatiotemporal data archive, while hydrogeophysics can provide spatiotemporal data on demand with the desired spatial and temporal resolution. The main disadvantage of RS and hydrogeophysical data is their uncertainty, which differs between the methods and applications, although reference in-situ measurements can substantially reduce that uncertainty.

Most spatial observations acquired with satellite RS can also be ac- quired through airborne or drone-borne hydrogeophysics. However, not every surface hydrogeophysical method can be implemented through airborne or drone-borne surveys (e.g. MRS cannot). Thanks to recent drone and sensors’ developments, drone-borne applications already cover most airborne applications. However, they still have limits, mainly due to restricted payloads and survey difficulties in extreme conditions.

IHMs use four types of data: i) climate forcings; ii) parameters; iii) boundary conditions, and iv) observations to constrain model state variables.
Climate forcings are optimally derived by a combination of RS archive time series data and ground-based, reference measurements used for bias correction.

Parameterisation of the ground surface and shallow soil can be conveniently derived from standard RS or from hydrogeophysics if higher resolution is needed. For deeper subsurface, only hydrogeophysics is suitable. In conductive environments, hydrostratigraphy of electrically conductive subsurface can be optimally done with EM methods, while in resistive environments, with GPR. Quantitative hydrogeological parameterisation of the deep subsurface is still restricted to the surface hydrogeophysical survey with MRS.

Boundary conditions are the most reliable if assigned along physical boundaries. Topographic catchment boundaries are typically derived from RS-based digital elevation models, geological and structural boundaries from hydrogeophysics, but surface water bodies either by RS or hydrogeophysics.

Surface water bodies are described by their bathymetry and temporal variability of stages; large surface water bodies (>100 m) can be assessed accurately by RS altimetry, but small ones (<100 m), by ICESat-2 and by airborne or drone-borne green LiDAR or GPR. Stream discharge can be derived from bathymetry and a time series of stages at the investigated stream section.

Actual evapotranspiration and soil moisture obtained from RS, or hydrogeophysics, are the most common non-classical observations applied to constrain IHM calibration or data assimilation. The main advantage of these RS and hydrogeophysical observations is that they provide semi-continuous spatial data coverage, while the main disadvantage is their uncertainty.

Unfortunately, head observations, the most common and classical observations, can be neither accurately defined by RS nor by hydrogeophysics.

In general, the temporal resolution of RS data is already sufficient for IHMs, but the spatial resolution, especially for 'applied' fine grid IHMs, is not always optimal. Continuous improvements in the spatial and temporal resolutions of RS platforms provide an opportunity for advancing the reliability of IHM calibration, data assimilation and predictions. In projects where RS does not provide sufficient spatial resolution, airborne or drone-borne hydrogeophysics offers alternative solutions, whereby the latter are particularly rapidly increasing.

We hope this review will contribute to the broader use of remote sensing and hydrogeophysical data in IHMs.

## CRediT authorship contribution statement

**M.W. Lubczynski:** Conceptualization, Writing – original draft, Writing – review & editing. **M. Leblanc:** Writing – review & editing. **O. Batelaan:** Writing – review & editing.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Data availability

No data was used for the research described in the article.

## Acknowledgement

First, the authors would like to thank two expert reviewers, Prof Dr. Peter Bouwer-Gottwein and Dr Alain Pascal Frances for their invaluable suggestions with references, which substantially contributed to the quality of this manuscript. We also thank two anonymous reviewers for their helpful comments. Finally, we would like to thank Dr Jean Roy, hydrogeophysical expert and friend of the first author, who was always

available for hydrogeophysical discussions.

## References

Abdollahipour, A., Ahmadi, H., Aminnejad, B., 2022. A review of downscaling methods of satellite-based precipitation estimates. Earth Sci. Inf. 15 (1), 1–20. https://doi.org/10.1007/s12145-021-00669-4.

Abolt, C., Caldwell, T., Wolaver, B., Pai, H., 2018. Unmanned aerial vehicle-based monitoring of groundwater inputs to surface waters using an economical thermal infrared camera. Opt. Eng. 57 (5) https://doi.org/10.1117/1.Oe.57.5.053113.

Acevo-Herrera, R., Aguasca, A., Bosch-Lluis, X., Camps, A., Martínez-Fernández, J., Sánchez-Martín, N., Pérez-Gutiérrez, C., 2010. Design and first results of an UAV-borne L-band radiometer for multiple monitoring purposes. Remote Sens. (Basel) 2 (7), 1662–1679.

Acharya, B.S., Bhandari, M., Bandini, F., Pizarro, A., Perks, M., Joshi, D.R., Wang, S., Dogwiler, T., Ray, R.L., Kharel, G., Sharma, S., 2021. Unmanned aerial vehicles in hydrology and water management: applications, challenges, and perspectives. Water Resour. Res. 57 (11) https://doi.org/10.1029/2021wr029925.

AghaKouchak, A., Mehran, A., Norouzi, H., Behrangi, A., 2012. Systematic and random error components in satellite precipitation data sets. Geophys. Res. Lett. 39 (9), L09406. https://doi.org/10.1029/2012GL051592.

Ahlfeld, D.P., Barlow, P.M., Baker, K.M., 2011. Documentation for the State Variables Package for the Groundwater-Management Process of MODFLOW-2005 (GWM-2005). 6-A36. DOI:10.3133/tm6A36.

Ahmad, M., Bastiaanssen, W.G.M., Feddes, R.A., 2005. A new technique to estimate net groundwater use across large irrigated areas by combining remote sensing and water balance approaches, rechna doab. Pakistan. Hydrogeology Journal 13 (5-6), 653–664. https://doi.org/10.1007/s10040-004-0394-5.

Ala-Aho, P., Rossi, P.M., Isokangas, E., Klove, B., 2015. Fully integrated surface-subsurface flow modelling of groundwater-lake interaction in an esker aquifer: model verification with stable isotopes and airborne thermal imaging. J. Hydrol. 522, 391–406. https://doi.org/10.1016/j.jhydrol.2014.12.054.

Ala-Aho, P., Soulsby, C., Wang, H.L., Tetzlaff, D., 2017. Integrated surface-subsurface model to investigate the role of groundwater in headwater catchment runoff generation: a minimalist approach to parameterisation. J. Hydrol. 547, 664–677. https://doi.org/10.1016/j.jhydrol.2017.02.023.

Alexakis, D.D., Mexis, F.-D.-K., Vozinaki, A.-E.-K., Daliakopoulos, I.N., Tsanis, I.K., 2017. Soil moisture content estimation based on Sentinel-1 and auxiliary earth observation products. A Hydrological Approach, Sensors. https://doi.org/10.3390/s17061455.

Allen, R., Pereira, L., Raes, D., Smith, M., 1998. Crop evapotranspiration - Guidelines for computing crop water requirements - FAO Irrigation and drainage paper 56. 92-5-104219-5. Rome.

Altenau, E.H., Pavelsky, T.M., Durand, M.T., Yang, X., Frasson, R.P.D.M., Bendezu, L., 2021. The surface water and ocean topography (SWOT) Mission River database (SWORD): a Global River network for satellite data products. Water Resour. Res. 57 (7), e2021WR030054. https://doi.org/10.1029/2021WR030054.

Anderson, M.P., Woessner, W.W., Hunt, R.J., 2015. Applied groundwater modeling: simulation of flow and advective transport, 631-631 pp. DOI:10.1016/C2009-0-21563-7.

Andrew, R., Guan, H., Batelaan, O., 2017. Estimation of GRACE water storage components by temporal decomposition. J. Hydrol. 552, 341–350. https://doi.org/10.1016/j.jhydrol.2017.06.016.

Annan, A.P., Cosway, S.W., Redman, J.D., 1991. Water table detection with ground-penetrating radar. SEG Technical Program Expanded Abstracts 1991, 494–496. https://doi.org/10.1190/1.1888793.

Arnold, J.G., Moriasi, D.N., Gassman, P.W., Abbaspour, K.C., White, M.J., Srinivasan, R., Santhi, C., Harmel, R.D., van Griensven, A., Van Liew, M.W., Kannan, N., Jha, M.K., 2012. SWAT: model use, calibration. And VALIDATION. Trans. ASABE 55 (4), 1491–1508.

Auken, E., Foged, N., Larsen, J.J., Lassen, K.V.T., Maurya, P.K., Dath, S.M., Eiskjær, T.T., 2019. ITEM – a towed transient electromagnetic system for detailed 3D imaging of the top 70 m of the subsurface. Geophysics 84 (1), E13–E22. https://doi.org/10.1190/geo2018-0355.1.

Bandini, F., Sunding, T.P., Linde, J., Smith, O., Jensen, I.K., Köppi, C.J., Butts, M., Bauer-Gottwein, P., 2020. Unmanned aerial system (UAS) observations of water surface elevation in a small stream: comparison of radar altimetry, LIDAR and photogrammetry techniques. Remote Sens. Environ. 237, 111487 https://doi.org/10.1016/j.rse.2019.111487.

Bandini, F., Kooij, L., Mortensen, B.K., Caspersen, M.B., Thomsen, L.G., Olesen, D., Bauer-Gottwein, P., 2023. Mapping inland water bathymetry with ground penetrating radar (GPR) on board unmanned aerial systems (UASs). J. Hydrol. 616, 128789 https://doi.org/10.1016/j.jhydrol.2022.128789.

Baroncini-Turricchia, G., Francés, A.P., Lubczynski, M.W., Martínez-Fernández, J., Roy, J., 2014. Integrating MRS data with hydrologic model - Carrizal Catchment, Spain. Near surface geophysics. DOI:10.3997/1873-0604.2014003.

Bastiaanssen, W.G.M., Menenti, M., Feddes, R.A., Holtslag, A.A.M., 1998. A remote sensing surface energy balance algorithm for land (SEBAL). 1. Formulation. Journal of Hydrology 212–213 (1-4), 198–212. https://doi.org/10.1016/S0022-1694(98)00253-4.

Batelaan, O., De Smedt, F., 2004. SEEPAGE, a new MODFLOW DRAIN package. Groundwater 42 (4), 576–588. https://doi.org/10.1111/j.1745-6584.2004.tb02626.x.

Beck, H.E., Wood, E.F., Pan, M., Fisher, C.K., Miralles, D.G., van Dijk, A.I.J.M., McVicar, T.R., Adler, R.F., 2019. MSWEP V2 global 3-hourly 0.1° precipitation:
Climate forcings are optimally derived by a combination of RS archive time series data and ground-based, reference measurements used for bias correction.

Parameterisation of the ground surface and shallow soil can be conveniently derived from standard RS or from hydrogeophysics if higher resolution is needed. For deeper subsurface, only hydrogeophysics is suitable. In conductive environments, hydrostratigraphy of electrically conductive subsurface can be optimally done with EM methods, while in resistive environments, with GPR. Quantitative hydrogeological parameterisation of the deep subsurface is still restricted to the surface hydrogeophysical survey with MRS.

Boundary conditions are the most reliable if assigned along physical boundaries. Topographic catchment boundaries are typically derived from RS-based digital elevation models, geological and structural boundaries from hydrogeophysics, but surface water bodies either by RS or hydrogeophysics.

Surface water bodies are described by their bathymetry and temporal variability of stages; large surface water bodies (>100 m) can be assessed accurately by RS altimetry, but small ones (<100 m), by ICESat-2 and by airborne or drone-borne green LiDAR or GPR. Stream discharge can be derived from bathymetry and a time series of stages at the investigated stream section.

Actual evapotranspiration and soil moisture obtained from RS, or hydrogeophysics, are the most common non-classical observations applied to constrain IHM calibration or data assimilation. The main advantage of these RS and hydrogeophysical observations is that they provide semi-continuous spatial data coverage, while the main disadvantage is their uncertainty.

Unfortunately, head observations, the most common and classical observations, can be neither accurately defined by RS nor by hydrogeophysics.

In general, the temporal resolution of RS data is already sufficient for IHMs, but the spatial resolution, especially for 'applied' fine grid IHMs, is not always optimal. Continuous improvements in the spatial and temporal resolutions of RS platforms provide an opportunity for advancing the reliability of IHM calibration, data assimilation and predictions. In projects where RS does not provide sufficient spatial resolution, airborne or drone-borne hydrogeophysics offers alternative solutions, whereby the latter are particularly rapidly increasing.

We hope this review will contribute to the broader use of remote sensing and hydrogeophysical data in IHMs.

## CRediT authorship contribution statement

**M.W. Lubczynski:** Conceptualization, Writing – original draft, Writing – review & editing. **M. Leblanc:** Writing – review & editing. **O. Batelaan:** Writing – review & editing.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Data availability

No data was used for the research described in the article.

## Acknowledgement

First, the authors would like to thank two expert reviewers, Prof Dr. Peter Bouwer-Gottwein and Dr Alain Pascal Frances for their invaluable suggestions with references, which substantially contributed to the quality of this manuscript. We also thank two anonymous reviewers for their helpful comments. Finally, we would like to thank Dr Jean Roy, hydrogeophysical expert and friend of the first author, who was always

available for hydrogeophysical discussions.

## References

Abdollahipour, A., Ahmadi, H., Aminnejad, B., 2022. A review of downscaling methods of satellite-based precipitation estimates. Earth Sci. Inf. 15 (1), 1–20. https://doi.org/10.1007/s12145-021-00669-4.

Abolt, C., Caldwell, T., Wolaver, B., Pai, H., 2018. Unmanned aerial vehicle-based monitoring of groundwater inputs to surface waters using an economical thermal infrared camera. Opt. Eng. 57 (5) https://doi.org/10.1117/1.Oe.57.5.053113.

Acevo-Herrera, R., Aguasca, A., Bosch-Lluis, X., Camps, A., Martínez-Fernández, J., Sánchez-Martín, N., Pérez-Gutiérrez, C., 2010. Design and first results of an UAV-borne L-band radiometer for multiple monitoring purposes. Remote Sens. (Basel) 2 (7), 1662–1679.

Acharya, B.S., Bhandari, M., Bandini, F., Pizarro, A., Perks, M., Joshi, D.R., Wang, S., Dogwiler, T., Ray, R.L., Kharel, G., Sharma, S., 2021. Unmanned aerial vehicles in hydrology and water management: applications, challenges, and perspectives. Water Resour. Res. 57 (11) https://doi.org/10.1029/2021wr029925.

AghaKouchak, A., Mehran, A., Norouzi, H., Behrangi, A., 2012. Systematic and random error components in satellite precipitation data sets. Geophys. Res. Lett. 39 (9), L09406. https://doi.org/10.1029/2012GL051592.

Ahlfeld, D.P., Barlow, P.M., Baker, K.M., 2011. Documentation for the State Variables Package for the Groundwater-Management Process of MODFLOW-2005 (GWM-2005). 6-A36. DOI:10.3133/tm6A36.

Ahmad, M., Bastiaanssen, W.G.M., Feddes, R.A., 2005. A new technique to estimate net groundwater use across large irrigated areas by combining remote sensing and water balance approaches, rechna doab. Pakistan. Hydrogeology Journal 13 (5-6), 653–664. https://doi.org/10.1007/s10040-004-0394-5.

Ala-Aho, P., Rossi, P.M., Isokangas, E., Klove, B., 2015. Fully integrated surface-subsurface flow modelling of groundwater-lake interaction in an esker aquifer: model verification with stable isotopes and airborne thermal imaging. J. Hydrol. 522, 391–406. https://doi.org/10.1016/j.jhydrol.2014.12.054.

Ala-Aho, P., Soulsby, C., Wang, H.L., Tetzlaff, D., 2017. Integrated surface-subsurface model to investigate the role of groundwater in headwater catchment runoff generation: a minimalist approach to parameterisation. J. Hydrol. 547, 664–677. https://doi.org/10.1016/j.jhydrol.2017.02.023.

Alexakis, D.D., Mexis, F.-D.-K., Vozinaki, A.-E.-K., Daliakopoulos, I.N., Tsanis, I.K., 2017. Soil moisture content estimation based on Sentinel-1 and auxiliary earth observation products. A Hydrological Approach, Sensors. https://doi.org/10.3390/s17061455.

Allen, R., Pereira, L., Raes, D., Smith, M., 1998. Crop evapotranspiration - Guidelines for computing crop water requirements - FAO Irrigation and drainage paper 56. 92-5-104219-5. Rome.

Altenau, E.H., Pavelsky, T.M., Durand, M.T., Yang, X., Frasson, R.P.D.M., Bendezu, L., 2021. The surface water and ocean topography (SWOT) Mission River database (SWORD): a Global River network for satellite data products. Water Resour. Res. 57 (7), e2021WR030054. https://doi.org/10.1029/2021WR030054.

Anderson, M.P., Woessner, W.W., Hunt, R.J., 2015. Applied groundwater modeling: simulation of flow and advective transport, 631-631 pp. DOI:10.1016/C2009-0-21563-7.

Andrew, R., Guan, H., Batelaan, O., 2017. Estimation of GRACE water storage components by temporal decomposition. J. Hydrol. 552, 341–350. https://doi.org/10.1016/j.jhydrol.2017.06.016.

Annan, A.P., Cosway, S.W., Redman, J.D., 1991. Water table detection with ground-penetrating radar. SEG Technical Program Expanded Abstracts 1991, 494–496. https://doi.org/10.1190/1.1888793.

Arnold, J.G., Moriasi, D.N., Gassman, P.W., Abbaspour, K.C., White, M.J., Srinivasan, R., Santhi, C., Harmel, R.D., van Griensven, A., Van Liew, M.W., Kannan, N., Jha, M.K., 2012. SWAT: model use, calibration. And VALIDATION. Trans. ASABE 55 (4), 1491–1508.

Auken, E., Foged, N., Larsen, J.J., Lassen, K.V.T., Maurya, P.K., Dath, S.M., Eiskjær, T.T., 2019. ITEM – a towed transient electromagnetic system for detailed 3D imaging of the top 70 m of the subsurface. Geophysics 84 (1), E13–E22. https://doi.org/10.1190/geo2018-0355.1.

Bandini, F., Sunding, T.P., Linde, J., Smith, O., Jensen, I.K., Köppl, C.J., Butts, M., Bauer-Gottwein, P., 2020. Unmanned aerial system (UAS) observations of water surface elevation in a small stream: comparison of radar altimetry, LIDAR and photogrammetry techniques. Remote Sens. Environ. 237, 111487 https://doi.org/10.1016/j.rse.2019.111487.

Bandini, F., Kooij, L., Mortensen, B.K., Caspersen, M.B., Thomsen, L.G., Olesen, D., Bauer-Gottwein, P., 2023. Mapping inland water bathymetry with ground penetrating radar (GPR) on board unmanned aerial systems (UASs). J. Hydrol. 616, 128789 https://doi.org/10.1016/j.jhydrol.2022.128789.

Baroncini-Turricchia, G., Francés, A.P., Lubczynski, M.W., Martínez-Fernández, J., Roy, J., 2014. Integrating MRS data with hydrologic model - Carrizal Catchment, Spain. Near surface geophysics. DOI:10.3997/1873-0604.2014003.

Bastiaanssen, W.G.M., Menenti, M., Feddes, R.A., Holtslag, A.A.M., 1998. A remote sensing surface energy balance algorithm for land (SEBAL). 1. Formulation. Journal of Hydrology 212–213 (1–4), 198–212. https://doi.org/10.1016/S0022-1694(98)00253-4.

Batelaan, O., De Smedt, F., 2004. SEEPAGE, a new MODFLOW DRAIN package. Groundwater 42 (4), 576–588. https://doi.org/10.1111/j.1745-6584.2004.tb02626.x.

Beck, H.E., Wood, E.F., Pan, M., Fisher, C.K., Miralles, D.G., van Dijk, A.I.J.M., McVicar, T.R., Adler, R.F., 2019. MSWEP V2 global 3-hourly 0.1° precipitation:
methodology and quantitative assessment. Bull. Am. Meteorol. Soc. 100 (3), 473-500. https://doi.org/10.1175/bams-d-17-0138.1.

Behroozmand, A.A., Auken, E., Fiandaca, G., Christiansen, A.V., 2012. Improvement in MRS parameter estimation by joint and laterally constrained inversion of MRS and TEM data. Geophysics 77 (4), WB191-WB200. https://doi.org/10.1190/geo2011-0404.1.

Bertoni, C., Lofi, J., Micallef, A., Moe, H., 2020. Seismic reflection methods in offshore groundwater research. Geosciences 10 (8). https://doi.org/10.3390/geosciences10082099.

Beven, K., Cloke, J., Pappenberger, F., Lamb, R., Hunter, N., 2015. Hyperresolution information and hyperresolution ignorance in modelling the hydrology of the land surface. Science China-Earth Sciences 58 (1), 25-35. https://doi.org/10.1007/s11430-014-5003-4.

Biancamaria, S., Lettenmaier, D.P., Pavelsky, T.M., 2016. The SWOT Mission and its capabilities for land hydrology. Surv. Geophys. 37 (2), 307-337. https://doi.org/10.1007/s10712-015-9346-y.

Bolanos-Sánchez, C., Prado-Hernández, J.V., Silván-Cárdenas, J.L., Vázquez-Pena, M.A., Madrigal-Gómez, J.M., Martínez-Rúz, A., 2021. Estimating rainfall interception of Pinus hartwegii and Abies religiosa using analytical models and point cloud. Forests 12 (7), 866.

Boucher, M., Favreau, G., Vouillamoz, J.M., Nazoum, Y., Legchenko, A., 2009. Estimating specific yield and transmissivity with magnetic resonance sounding in an unconfined sandstone aquifer (Niger). Hydrgeol. J. 17 (7), 1805-1815. https://doi.org/10.1007/s10040-009-0447-x.

Boucher, M., Favreau, G., Nazoum, Y., Cappelaere, B., Massuel, S., Legchenko, A., 2012. Constraining groundwater modeling with magnetic resonance soundings. Ground Water 50 (5), 775-784. https://doi.org/10.1111/j.1745-6584.2011.00891.x.

Bousbih, S., Zribi, M., Pelletier, C., Gorrab, A., Lili-Chabaane, Z., Baghdadi, N., Ben Aissa, N., Mougenot, B., 2019. Soil texture estimation using radar and optical data from Sentinel-1 and Sentinel-2. Remote Sens. (Basel) 11 (13), 1520.

Bronstert, A., Heistermann, M., Francke, T., 2017. The sense and non-sense of plot-scale, catchment-scale, continental-scale and global-scale hydrological modelling, pp. 3655.

Camera, C., Bruggeman, A., Hadjinicolaou, P., Pashiardis, S., Lange, M.A., 2014. Evaluation of interpolation techniques for the creation of gridded daily precipitation (1 × 1 km2); Cyprus, 1980-2010. J. Geophys. Res. Atmos. 119 (2), 693-712. https://doi.org/10.1002/2013JD020611.

Camporese, M., Girotto, M., 2022. Recent advances and opportunities in data assimilation for physics-based hydrological modeling. Frontiers in Water 4, 948832. https://doi.org/10.3389/frwa.2022.948832.

Camporese, M., Paniconi, C., Putti, M., Orlandini, S., 2010. Surface-subsurface flow modeling with path-based runoff routing, boundary condition-based coupling, and assimilation of multisource observation data. Water Resour. Res. 46 (2), 2512. https://doi.org/10.1029/2008WR007536.

Campos, I., Neale, C.M.U., Calera, A., Balbontín, C., González-Piqueras, J., 2010. Assessing satellite-based basal crop coefficients for irrigated grapes (Vitis vinifera L.). Agric Water Manag 98 (1), 45-54. https://doi.org/10.1016/j.agwat.2010.07.011.

Campos, I., Villodre, J., Carrara, A., Calera, A., 2013. Remote sensing-based soil water balance to estimate Mediterranean holm oak savanna (dehesa) evapotranspiration under water stress conditions. J. Hydrol. 494, 1-9. https://doi.org/10.1016/j.jhydrol.2013.04.033.

Carrera-Hernández, J.J., 2021. Not all DEMs are equal: an evaluation of 50 globally available 30 m resolution DEMs with geodetic benchmarks and LiDAR in Mexico. Remote Sens. Environ. 261, 112474 https://doi.org/10.1016/j.rse.2021.112474.

Chen, S.C., Arrouays, D., Mulder, V.L., Poggio, L., Minasny, B., Roudier, P., Libohová, Z., Lagacherie, P., Shi, Z., Hannam, J., Meersmans, J., Richer-de-Forges, A.C., Walter, C., 2022. Digital mapping of GlobalSoilMap soil properties at a broad scale: a review. Geoderma 409. https://doi.org/10.1016/j.geoderma.2021.115567.

Chongo, M., Christiansen, A.V., Tembo, A., Banda, K.E., Nyambe, I.A., Larsen, F., Bauer-Gottwein, P., 2015. Airborne and ground-based transient electromagnetic modeling of groundwater salinity in the machile-Zambezi Basin, southwestern Zambia. Near Surf. Geophys. 13 (4), 383-395. https://doi.org/10.3997/1873-0604.2015024.

Choudhury, B.J., Ahmed, N.U., Idso, S.B., Reginato, R.J., Daughtry, C.S.T., 1994. Relations between evaporation coefficients and vegetation indices studied by model simulations. Remote Sens. Environ. 50 (1), 1-17. https://doi.org/10.1016/0034-4257(94)90090-6.

Christensen, N.K., Christensen, S., Ferre, T.P.A., 2016. Testing alternative uses of electromagnetic data to reduce the prediction error of groundwater models. Hydrol. Earth Syst. Sci. 20 (5), 1925-1946. https://doi.org/10.5194/hess-20-1925-2016.

Christiansen, A.V., Auken, S., Sørensen, K., 2006. The transient electromagnetic method. In: Rirsch, R. (Ed.), Groundwater Geophysics: A Tool for Hydrogeology. Springer, Berlin Heidelberg, Berlin, Heidelberg, pp. 179-225. https://doi.org/10.1007/3-540-29387-6-6.

Christiansen, L., Binning, P.J., Rosbjerg, D., Andersen, O.B., Bauer-Gottwein, P., 2011a. Using time-lapse gravity for groundwater model calibration: an application to alluvial aquifer storage. Water Resour. Res. 47 https://doi.org/10.1029/2011wr009859.

Christiansen, L., Lund, S., Andersen, O.B., Binning, P.J., Rosbjerg, D., Bauer-Gottwein, P., 2011b. Measuring gravity change caused by water storage variations: performance assessment under controlled conditions. J. Hydrol. 402 (1-2), 60-70. https://doi.org/10.1016/j.jhydrol.2011.03.004.

Christoffersen, L., Bauer-Gottwein, P., Sørensen, L.S., Nielsen, K., 2023. ICE2WSS; an R package for estimating river water surface slopes from ICESat-2. Environ. Model. Softw. 168, 105789 https://doi.org/10.1016/j.envsoft.2023.105789.

Coppo Frias, M., Liu, S., Mo, X., Nielsen, K., Ranndal, H., Jiang, L., Ma, J., Bauer-Gottwein, P., 2023. River hydraulic modeling with ICESat-2 land and water surface elevation. Hydrol. Earth Syst. Sci. 27 (5), 1011-1032. https://doi.org/10.5194/hess-27-1011-2023.

Creutzfeldt, B., Güntner, A., Vorogushyn, S., Merz, B., 2010. The benefits of graviometer observations for modelling water storage changes at the field scale. Hydrol. Earth Syst. Sci. 14 (9), 1715-1730. https://doi.org/10.5194/hess-14-1715-2010.

Crouse, R.P., Corbett, E.S., Seegrist, D.W., 1966. METHODS of measuring and analyzing rainfall interception by grass. International Association of Scientific Hydrology. Bulletin 11 (2), 110-120. https://doi.org/10.1080/02626666094934634.

Cui, Y.K., Jia, L., 2014. A modified gash model for estimating rainfall interception loss of Forest using remote sensing observations at regional scale. Water 6 (4), 993-1012. https://doi.org/10.3390/w6040993.

Cui, Y.K., Jia, L., Hu, G.C., Zhou, J., 2015. Mapping of interception loss of vegetation in the Heihe River basin of China using remote sensing observations. IEEE Geosci. Remote Sens. Lett. 12 (1), 23-27. https://doi.org/10.1109/lgrs.2014.2324635.

Dam, D., Christensen, S., 2003. Including geophysical data in ground water model inverse calibration. Groundwater 41 (2), 178-189. https://doi.org/10.1111/j.1745-6584.2003.tb02581.x.

Daoud, M.G., Lubczynski, M.W., Zoltan, V., Francés, A.P., 2022. Application of a novel cascade-routing and refiltration concept with a voronoi unstructured grid in MODFLOW 6, for an assessment of surface-water/groundwater interactions in a hard-rock catchment (sardon, Spain). Hydrgeol. J. 1-27 https://doi.org/10.1007/s10040-021-02430-z.

Dassargues, A., 2018. Hydrogeology Groundwater science and engineering. CRC Press, Boca Raton, 492-492 pp. DOI:10.1201/9780429470660.

De Lannoy, G.J.M., Bechtold, M., Albergel, C., Brocca, L., Calvet, J.-C., Carrassi, A., Crow, W.T., de Rosnay, P., Durand, M., Forman, B., Geppert, G., Girotto, M., Hendricks Franssen, J.-J., Jonas, T., Kumar, S., Lievens, H., Lu, Y., Massari, C., Pauwels, V.R.N., Reichle, R.H., Steele-Dunne, S., 2022. Perspective on satellite-based land data assimilation to estimate water cycle components in an era of advanced data availability and model sophistication. Frontiers in Water 4. https://doi.org/10.3389/frwa.2022.981745.

Deidda, C., De Michele, C., Arslan, A.N., Pecora, S., Taburet, N., 2021. Accuracy of copernicus altimeter water level data in italian Rivers accounting for Narrow River sections. Remote Sens. (Basel) 13 (21). https://doi.org/10.3390/rs13214456.

Dembele, M., Hrachowitz, M., Savenije, H.H.G., Mariethoz, G., Schaefli, B., 2020. Improving the predictive skill of a distributed hydrological model by calibration on spatial patterns with multiple satellite data sets. Water Resour. Res. 56 (1) https://doi.org/10.1029/2019wr026085.

Demir, G., Friesen, J., Filipizik, J., Michalzik, B., Hildebrandt, A., 2022. A method proposal for throughfall measurement in grassland at plot scale in temperate climate: 'interception tubes'. Front. Earth Sci. 10 https://doi.org/10.3389/feart.2022.799419.

Derin, Y., Yilmaz, K.K., 2014. Evaluation of multiple satellite-based precipitation products over complex topography. J. Hydrometeorol. 15 (4), 1498-1516. https://doi.org/10.1175/jhm-d-13-0191.1.

Dettmering, D., Ellenbeck, L., Scherer, D., Schwatke, C., Niemann, C., 2020. Potential and limitations of satellite altimetry constellations for monitoring surface water storage changes-a case study in the Mississippi Basin. Remote Sens. (Basel) 12 (20). https://doi.org/10.3390/rs12203320.

Dewitte, O., Jones, A., Elbelrhiti, H., Horion, S., Montanarella, L., 2012. Satellite remote sensing for soil mapping in Africa: an overview. Progress in Physical Geography- Earth and Environment 36 (4), 514-538. https://doi.org/10.1177/0309133312446981.

Di, Z., Maggioni, V., Mei, Y., Vazquez, M., Houser, P., Emelianenko, M., 2020. Centroidal voronoi tessellation based methods for optimal rain gauge location prediction. J. Hydrol. 584, 124651 https://doi.org/10.1016/j.jhydrol.2020.124651.

Dickson, N.E.M., Comte, J.C., McKinley, J., Oftenderger, U., 2014. Coupling ground and airborne geophysical data with upscaling techniques for regional groundwater modeling of heterogeneous aquifers: case study of a sedimentary aquifer intruded by volcanic dykes in Northern Ireland. Water Resour. Res. 50 (10), 7984-8001. https://doi.org/10.1002/2014wr015320.

Doherty, J., Moore, C., 2020. Decision support modeling: data assimilation, uncertainty quantification, and strategic abstraction. Groundwater 58 (3), 327-337. https://doi.org/10.1111/gwat.12969.

Döll, P., Douville, H., Güntner, A., Müller Schmied, H., Wada, Y., 2016. Modelling freshwater resources at the global scale: challenges and prospects. Surv. Geophys. 37 (2), 195-221. https://doi.org/10.1007/s10712-015-9343-1.

Duan, M.Q., Song, X.Y., Liu, X.W., Cui, D.J., Zhang, X.G., 2022. Mapping the soil types combining multi-temporal remote sensing data with texture features. Comput. Electron. Agric. 200 https://doi.org/10.1016/j.compag.2022.107230.

Eliades, M., Bruggeman, A., Djuma, H., Christou, A., Rovanias, K., Lubczynski, M.W., 2022. Testing three rainfall interception models and different parameterization methods with data from an open Mediterranean pine forest. Agric. For. Meteorol. 313, 108755 https://doi.org/10.1016/j.agrformet.2021.108755.

Enemark, T., Peeters, L.J.M., Mallants, D., Batelaan, O., 2019. Hydrogeological conceptual model building and testing: a review. J. Hydrol. 569, 310-329. https://doi.org/10.1016/j.jhydrol.2018.12.007.

Enemark, T., Peeters, L., Mallants, D., Flinchin, B., Batelaan, O., 2020. A systematic approach to hydrogeological conceptual model testing, combining remote sensing and geophysical data. Water Resour. Res. 56 (8) https://doi.org/10.1029/2020wr027578.
methodology and quantitative assessment. Bull. Am. Meteorol. Soc. 100 (3), 473-500. https://doi.org/10.1175/bams-d-17-0138.1.

Behroozmand, A.A., Auken, E., Fiandaca, G., Christiansen, A.V., 2012. Improvement in MRS parameter estimation by joint and laterally constrained inversion of MRS and TEM data. Geophysics 77 (4), WB191-WB200. https://doi.org/10.1190/geo2011-0404.1.

Bertoni, C., Lofi, J., Micallef, A., Moe, H., 2020. Seismic reflection methods in offshore groundwater research. Geosciences 10 (8). https://doi.org/10.3390/geosciences10082099.

Beven, K., Cloke, J., Pappenberger, F., Lamb, R., Hunter, N., 2015. Hyperresolution information and hyperresolution ignorance in modelling the hydrology of the land surface. Science China-Earth Sciences 58 (1), 25-35. https://doi.org/10.1007/s11430-014-5003-4.

Biancamaria, S., Lettenmaier, D.P., Pavelsky, T.M., 2016. The SWOT Mission and its capabilities for land hydrology. Surv. Geophys. 37 (2), 307-337. https://doi.org/10.1007/s10712-015-9346-y.

Bolanos-Sánchez, C., Prado-Hernández, J.V., Silván-Cárdenas, J.L., Vázquez-Pena, M.A., Madrigal-Gómez, J.M., Martínez-Rúz, A., 2021. Estimating rainfall interception of Pinus hartwegii and Abies religiosa using analytical models and point cloud. Forests 12 (7), 866.

Boucher, M., Favreau, G., Vouillamoz, J.M., Nazoum, Y., Legchenko, A., 2009. Estimating specific yield and transmissivity with magnetic resonance sounding in an unconfined sandstone aquifer (Niger). Hydrgeol. J. 17 (7), 1805-1815. https://doi.org/10.1007/s10040-009-0447-x.

Boucher, M., Favreau, G., Nazoum, Y., Cappelaere, B., Massuel, S., Legchenko, A., 2012. Constraining groundwater modeling with magnetic resonance soundings. Ground Water 50 (5), 775-784. https://doi.org/10.1111/j.1745-6584.2011.00891.x.

Bousbih, S., Zribi, M., Pelletier, C., Gorrab, A., Lili-Chabaane, Z., Baghdadi, N., Ben Aissa, N., Mougenot, B., 2019. Soil texture estimation using radar and optical data from Sentinel-1 and Sentinel-2. Remote Sens. (Basel) 11 (13), 1520.

Bronstert, A., Heistermann, M., Francke, T., 2017. The sense and non-sense of plot-scale, catchment-scale, continental-scale and global-scale hydrological modelling, pp. 3655.

Camera, C., Bruggeman, A., Hadjinicolaou, P., Pashiardis, S., Lange, M.A., 2014. Evaluation of interpolation techniques for the creation of gridded daily precipitation (1 × 1 km2); Cyprus, 1980-2010. J. Geophys. Res. Atmos. 119 (2), 693-712. https://doi.org/10.1002/2013JD020611.

Camporese, M., Girotto, M., 2022. Recent advances and opportunities in data assimilation for physics-based hydrological modeling. Frontiers in Water 4, 948832. https://doi.org/10.3389/frwa.2022.948832.

Camporese, M., Paniconi, C., Putti, M., Orlandini, S., 2010. Surface-subsurface flow modeling with path-based runoff routing, boundary condition-based coupling, and assimilation of multisource observation data. Water Resour. Res. 46 (2), 2512. https://doi.org/10.1029/2008WR007536.

Campos, I., Neale, C.M.U., Calera, A., Balbontín, C., González-Piqueras, J., 2010. Assessing satellite-based basal crop coefficients for irrigated grapes (Vitis vinifera L.). Agric Water Manag 98 (1), 45-54. https://doi.org/10.1016/j.agwat.2010.07.011.

Campos, I., Villodre, J., Carrara, A., Calera, A., 2013. Remote sensing-based soil water balance to estimate Mediterranean holm oak savanna (dehesa) evapotranspiration under water stress conditions. J. Hydrol. 494, 1-9. https://doi.org/10.1016/j.jhydrol.2013.04.033.

Carrera-Hernández, J.J., 2021. Not all DEMs are equal: an evaluation of 50 globally available 30 m resolution DEMs with geodetic benchmarks and LiDAR in Mexico. Remote Sens. Environ. 261, 112474 https://doi.org/10.1016/j.rse.2021.112474.

Chen, S.C., Arrouays, D., Mulder, V.L., Poggio, L., Minasny, B., Roudier, P., Libohová, Z., Lagacherie, P., Shi, Z., Hannam, J., Meersmans, J., Richer-de-Forges, A.C., Walter, C., 2022. Digital mapping of GlobalSoilMap soil properties at a broad scale: a review. Geoderma 409. https://doi.org/10.1016/j.geoderma.2021.115567.

Chongo, M., Christiansen, A.V., Tembo, A., Banda, K.E., Nyambe, I.A., Larsen, F., Bauer-Gottwein, P., 2015. Airborne and ground-based transient electromagnetic modeling of groundwater salinity in the machile-Zambezi Basin, southwestern Zambia. Near Surf. Geophys. 13 (4), 383-395. https://doi.org/10.3997/1873-0604.2015024.

Choudhury, B.J., Ahmed, N.U., Idso, S.B., Reginato, R.J., Daughtry, C.S.T., 1994. Relations between evaporation coefficients and vegetation indices studied by model simulations. Remote Sens. Environ. 50 (1), 1-17. https://doi.org/10.1016/0034-4257(94)90090-6.

Christensen, N.K., Christensen, S., Ferre, T.P.A., 2016. Testing alternative uses of electromagnetic data to reduce the prediction error of groundwater models. Hydrol. Earth Syst. Sci. 20 (5), 1925-1946. https://doi.org/10.5194/hess-20-1925-2016.

Christiansen, A.V., Auken, S., Sørensen, K., 2006. The transient electromagnetic method. In: Rirsch, R. (Ed.), Groundwater Geophysics: A Tool for Hydrogeology. Springer, Berlin Heidelberg, Berlin, Heidelberg, pp. 179-225. https://doi.org/10.1007/3-540-29387-6-6.

Christiansen, L., Binning, P.J., Rosbjerg, D., Andersen, O.B., Bauer-Gottwein, P., 2011a. Using time-lapse gravity for groundwater model calibration: an application to alluvial aquifer storage. Water Resour. Res. 47 https://doi.org/10.1029/2011wr009859.

Christiansen, L., Lund, S., Andersen, O.B., Binning, P.J., Rosbjerg, D., Bauer-Gottwein, P., 2011b. Measuring gravity change caused by water storage variations: performance assessment under controlled conditions. J. Hydrol. 402 (1-2), 60-70. https://doi.org/10.1016/j.jhydrol.2011.03.004.

Christoffersen, L., Bauer-Gottwein, P., Sørensen, L.S., Nielsen, K., 2023. ICE2WSS; an R package for estimating river water surface slopes from ICESat-2. Environ. Model. Softw. 168, 105789 https://doi.org/10.1016/j.envsoft.2023.105789.

Coppo Frias, M., Liu, S., Mo, X., Nielsen, K., Ranndal, H., Jiang, L., Ma, J., Bauer-Gottwein, P., 2023. River hydraulic modeling with ICESat-2 land and water surface elevation. Hydrol. Earth Syst. Sci. 27 (5), 1011-1032. https://doi.org/10.5194/hess-27-1011-2023.

Creutzfeldt, B., Güntner, A., Vorogushyn, S., Merz, B., 2010. The benefits of graviometer observations for modelling water storage changes at the field scale. Hydrol. Earth Syst. Sci. 14 (9), 1715-1730. https://doi.org/10.5194/hess-14-1715-2010.

Crouse, R.P., Corbett, E.S., Seegrist, D.W., 1966. METHODS of measuring and analyzing rainfall interception by grass. International Association of Scientific Hydrology. Bulletin 11 (2), 110-120. https://doi.org/10.1080/02626666094934634.

Cui, Y.K., Jia, L., 2014. A modified gash model for estimating rainfall interception loss of Forest using remote sensing observations at regional scale. Water 6 (4), 993-1012. https://doi.org/10.3390/w6040993.

Cui, Y.K., Jia, L., Hu, G.C., Zhou, J., 2015. Mapping of interception loss of vegetation in the Heihe River basin of China using remote sensing observations. IEEE Geosci. Remote Sens. Lett. 12 (1), 23-27. https://doi.org/10.1109/lgrs.2014.2324635.

Dam, D., Christensen, S., 2003. Including geophysical data in ground water model inverse calibration. Groundwater 41 (2), 178-189. https://doi.org/10.1111/j.1745-6584.2003.tb02581.x.

Daoud, M.G., Lubczynski, M.W., Zoltan, V., Francés, A.P., 2022. Application of a novel cascade-routing and refiltration concept with a voronoi unstructured grid in MODFLOW 6, for an assessment of surface-water/groundwater interactions in a hard-rock catchment (sardon, Spain). Hydrgeol. J. 1-27 https://doi.org/10.1007/s10040-021-02430-z.

Dassargues, A., 2018. Hydrogeology Groundwater science and engineering. CRC Press, Boca Raton, 492-492 pp. DOI:10.1201/9780429470660.

De Lannoy, G.J.M., Bechtold, M., Albergel, C., Brocca, L., Calvet, J.-C., Carrassi, A., Crow, W.T., de Rosnay, P., Durand, M., Forman, B., Geppert, G., Girotto, M., Hendricks Franssen, J.-J., Jonas, T., Kumar, S., Lievens, H., Lu, Y., Massari, C., Pauwels, V.R.N., Reichle, R.H., Steele-Dunne, S., 2022. Perspective on satellite-based land data assimilation to estimate water cycle components in an era of advanced data availability and model sophistication. Frontiers in Water 4. https://doi.org/10.3389/frwa.2022.981745.

Deidda, C., De Michele, C., Arslan, A.N., Pecora, S., Taburet, N., 2021. Accuracy of copernicus altimeter water level data in italian Rivers accounting for Narrow River sections. Remote Sens. (Basel) 13 (21). https://doi.org/10.3390/rs13214456.

Dembele, M., Hrachowitz, M., Savenije, H.H.G., Mariethoz, G., Schaefli, B., 2020. Improving the predictive skill of a distributed hydrological model by calibration on spatial patterns with multiple satellite data sets. Water Resour. Res. 56 (1) https://doi.org/10.1029/2019wr026085.

Demir, G., Friesen, J., Filipizik, J., Michalzik, B., Hildebrandt, A., 2022. A method proposal for throughfall measurement in grassland at plot scale in temperate climate: 'interception tubes'. Front. Earth Sci. 10 https://doi.org/10.3389/feart.2022.799419.

Derin, Y., Yilmaz, K.K., 2014. Evaluation of multiple satellite-based precipitation products over complex topography. J. Hydrometeorol. 15 (4), 1498-1516. https://doi.org/10.1175/jhm-d-13-0191.1.

Dettmering, D., Ellenbeck, L., Scherer, D., Schwatke, C., Niemann, C., 2020. Potential and limitations of satellite altimetry constellations for monitoring surface water storage changes-a case study in the Mississippi Basin. Remote Sens. (Basel) 12 (20). https://doi.org/10.3390/rs12203320.

Dewitte, O., Jones, A., Elbelrhiti, H., Horion, S., Montanarella, L., 2012. Satellite remote sensing for soil mapping in Africa: an overview. Progress in Physical Geography-Earth and Environment 36 (4), 514-538. https://doi.org/10.1177/0309133312446981.

Di, Z., Maggioni, V., Mei, Y., Vazquez, M., Houser, P., Emelianenko, M., 2020. Centroidal voronoi tessellation based methods for optimal rain gauge location prediction. J. Hydrol. 584, 124651 https://doi.org/10.1016/j.jhydrol.2020.124651.

Dickson, N.E.M., Comte, J.C., McKinley, J., Oftenderger, U., 2014. Coupling ground and airborne geophysical data with upscaling techniques for regional groundwater modeling of heterogeneous aquifers: case study of a sedimentary aquifer intruded by volcanic dykes in Northern Ireland. Water Resour. Res. 50 (10), 7984-8001. https://doi.org/10.1002/2014wr015320.

Doherty, J., Moore, C., 2020. Decision support modeling: data assimilation, uncertainty quantification, and strategic abstraction. Groundwater 58 (3), 327-337. https://doi.org/10.1111/gwat.12969.

Döll, P., Douville, H., Güntner, A., Müller Schmied, H., Wada, Y., 2016. Modelling freshwater resources at the global scale: challenges and prospects. Surv. Geophys. 37 (2), 195-221. https://doi.org/10.1007/s10712-015-9343-1.

Duan, M.Q., Song, X.Y., Liu, X.W., Cui, D.J., Zhang, X.G., 2022. Mapping the soil types combining multi-temporal remote sensing data with texture features. Comput. Electron. Agric. 200 https://doi.org/10.1016/j.compag.2022.107230.

Eliades, M., Bruggeman, A., Djuma, H., Christou, A., Rovanias, K., Lubczynski, M.W., 2022. Testing three rainfall interception models and different parameterization methods with data from an open Mediterranean pine forest. Agric. For. Meteorol. 313, 108755 https://doi.org/10.1016/j.agrformet.2021.108755.

Enemark, T., Peeters, L.J.M., Mallants, D., Batelaan, O., 2019. Hydrogeological conceptual model building and testing: a review. J. Hydrol. 569, 310-329. https://doi.org/10.1016/j.jhydrol.2018.12.007.

Enemark, T., Peeters, L., Mallants, D., Flinchin, B., Batelaan, O., 2020. A systematic approach to hydrogeological conceptual model testing, combining remote sensing and geophysical data. Water Resour. Res. 56 (8) https://doi.org/10.1029/2020wr027578.
Ezzy, T.R., Cox, M.E., O'Rourke, A.J., Huftile, G.J., 2006. Groundwater flow modelling within a coastal alluvial plain setting using a high-resolution hydrofacies approach; Bells Creek plain. Australia. Hydrogeology Journal 14 (5), 675-688. https://doi.org/10.1007/s10040-005-0470-5.

Finger, D., Vis, M., Huss, M., Seibert, J., 2015. The value of multiple data set calibration versus model complexity for improving the performance of hydrological models in mountain catchments. Water Resour. Res. 51 (4), 1939-1958. https://doi.org/10.1002/2014wr015712.

Flinchum, B.A., Banks, E., Hatch, M., Batelaen, O., Peeters, L.J.M., Pasquet, S., 2020. Identifying recharge under subtle ephemeral features in a flat-lying semi-arid region using a combined geophysical approach. Hydrol. Earth Syst. Sci. 24 (9), 4353-4368. https://doi.org/10.5194/hess-24-4353-2020.

Fores, B., Champollion, C., Le Moigne, N., Bayer, R., Chéry, J., 2016. Assessing the precision of the iGrav superconducting gravimeter for hydrological models and karstic hydrological process identification. Geophys. J. Int. 208 (1), 269-280. https://doi.org/10.1093/gji/ggw396.

Forte, E., Bondini, M.B., Bortoletto, A., Dossi, M., Colucci, R.R., 2019. Pros and cons in helicopter-borne GPR data acquisition on rugged mountainous areas: critical analysis and practical guidelines. Pure Appl. Geophys. 176 (10), 4533-4554. https://doi.org/10.1007/s00024-019-02196-2.

Foti, S., Comina, C., Boiero, D., Socco, L.V., 2009. Non-uniqueness in surface-wave inversion and consequences on seismic site response analyses. Soil Dyn. Earthq. Eng. 29 (6), 982-993. https://doi.org/10.1016/j.soldyn.2008.11.004.

Francés, A.P., Lubczynski, M.W., 2011. Topsoil thickness prediction at the catchment scale by integration of invasive sampling, surface geophysics, remote sensing and statistical modeling. J. Hydrol. 405 (1), 31-47. https://doi.org/10.1016/j.jhydrol.2011.05.006.

Francés, A.P., Lubczynski, M.W., Roy, J., Santos, F.A.M., Mahmoudzadeh Ardekani, M. R., 2014. Hydrogeophysics and remote sensing for the design of hydrogeological conceptual models in hard rocks - sardón catchment (Spain). J. Appl. Geophys. 110, 63-81. https://doi.org/10.1016/j.JAPPGEO.2014.08.015.

Frances, A.P., Ramalho, E.C., Fernandes, J., Groen, M., Hugman, R., Khalil, M.A., De Plaan, J., Santos, F.A.M., 2015. Contributions of hydrogeophysics to the hydrogeological conceptual model of the Albufera-Ribeira de Quarteira coastal aquifer in Algarve. Portugal. Hydrogeology Journal 23 (7), 1553-1572. https://doi.org/10.1007/s10040-015-1282-x.

Frappart, F., Ramillien, G., 2018. Monitoring groundwater storage changes using the gravity recovery and climate experiment (GRACE). Satellite Mission: A Review. Remote Sensing 10 (6). https://doi.org/10.3390/rs10060829.

Gallistl, J., Schwindt, D., Birgit, J., Aigner, L., Pererson, M., Flores Orozco, A., 2022. Quantification of soil textural and hydraulic properties in a complex conductivity imaging framework: results from the wolfsberg slope. Front. Earth Sci. 10 https://doi.org/10.3389/feart.2022.911611.

Garambois, S., Visoin, C., Guzman, M.A.R., Brito, D., Guiller, B., Refloch, A., 2019. Analysis of ballistic waves in seismic noise monitoring of water table variations in a water field site: added value from numerical modelling to data understanding. Geophys. J. Int. 219 (3), 1636-1647. https://doi.org/10.1093/gji/ggz391.

Garkoti, A., Kundapura, S., 2021. Deriving water level data and discharge estimation using satellite altimetry for Krishna River. Karnataka. Remote Sensing Applications-Society and Environment 22. https://doi.org/10.1016/j.rsse.2021.100487.

Gash, J.H.C., Lloyd, C.R., Lachaud, G., 1995. Estimating sparse forest rainfall interception using an analytical model. J. Hydrol. 170 (1-4), 79-86. https://doi.org/10.1016/0022-1694(95)02697-N.

Gaur, S., Singh, B., Bandyopadhyay, A., Stisen, S., Singh, R., 2022. Spatial pattern-based performance evaluation and uncertainty analysis of a distributed hydrological model. Hydrol. Process. 36 (5) https://doi.org/10.1002/hyp.14586.

Gebremedhin, M.A., Lubczynski, M.W., Maathuis, B.P., Teka, D., 2021. Novel approach to integrate daily satellite rainfall with in-situ rainfall, upper Kekeze Basin. Ethiopia. Atmospheric Research 248. https://doi.org/10.1016/j.atmosres.2020.105135.

Gebremedhin, M.A., Lubczynski, M.W., Maathuis, B.H.P., Teka, D., 2022. Deriving potential evapotranspiration from satellite-based reference evapotranspiration, upper Kekeze Basin, northern Ethiopia. J. Hydrol.: Reg. Stud. 41 https://doi.org/10.1016/j.ejrh.2022.101059.

Gebremedhin, M.A., Lubczynski, M.W., Maathuis, B.H.P., Daoud, M.G., Teka, D., 2023. Spatio-temporal rainfall interception loss at the catchment scale from earth observation in a data-scarce area, northern Ethiopia. J. Hydrol. 626, 130126 https://doi.org/10.1016/j.jhydrol.2023.130126.

Gelsinari, S., Doble, R., Daly, E., Pauwels, V.R.N., 2020. Feasibility of improving groundwater modeling by assimilating evapotranspiration rates. Water Resour. Res. 56 (2) https://doi.org/10.1029/2019wr025983.

Gelsinari, S., Pauwels, V.R.N., Daly, E., van Dam, J., Uijlenhoet, R., Fewster-Young, N., Doble, R., 2021. Unsaturated zone model complexity for the assimilation of evapotranspiration rates in groundwater modelling. Hydrol. Earth Syst. Sci. 25 (4), 2261-2277. https://doi.org/10.5194/hess-25-2261-2021.

Gelsinari, S., Doody, T.M., Thompson, S.E., Doble, R., Daly, E., Pauwels, V.R.N., 2022. Informing hydrogeological models with remotely sensed evapotranspiration. Frontiers in Water 4. https://doi.org/10.3389/frwa.2022.932641.

Gerrits, A.M.J., Pfister, L., Savenije, H.H.G., 2010. Spatial and temporal variability of canopy and forest floor interception in a beech forest. Hydrol. Process. 24 (21), 3011-3025. https://doi.org/10.1002/hyp.7712.

Ghimire, C.P., Bruijnzeel, L.A., Lubczynski, M.W., Bonell, M., 2012. Rainfall interception by natural and planted forests in the Middle Mountains of Central Nepal. J. Hydrol. 475, 270-280. https://doi.org/10.1016/j.jhydrol.2012.09.051.

Ghimire, C.P., Adrian Bruijnzeel, L., Lubczynski, M.W., Ravelona, M., Zwartendijk, B.W., van Meerveld, H.J., 2017. Measurement and modeling of rainfall interception by two

differently aged secondary forests in upland eastern Madagascar. J. Hydrol. 545, 212-225. https://doi.org/10.1016/j.jhydrol.2016.10.032.

Ghimire, U., Srinivasan, G., Agarwal, A., 2019. Assessment of rainfall bias correction techniques for improved hydrological simulation. Int. J. Climatol. 39 (4), 2386-2399. https://doi.org/10.1002/joc.5959.

Gong, Y., Yang, K., Lin, Z., Fang, S., Wu, X., Zhu, R., Peng, Y., 2021. Remote estimation of leaf area index (LAI) with unmanned aerial vehicle (UAV) imaging for different rice cultivars throughout the entire growing season. Plant Methods 17 (1), 88. https://doi.org/10.1186/s13007-021-00789-4.

Grombacher, D., Liu, L., Griffiths, M.P., Vang, M.O., Larsen, J.J., 2021. Steady-state surface NMR for mapping of groundwater. Geophys. Res. Lett. 48 (23) https://doi.org/10.1029/2021gl095381.

Grombacher, D., Griffiths, M.P., Liu, L., Vang, M.O., Larsen, J.J., 2022. Frequency shifting steady-state surface NMR signals to avoid problematic narrowband-noise sources. Geophys. Res. Lett. 49 (7) https://doi.org/10.1029/2021gl097402.

Güntner, A., Reich, M., Mikolaj, M., Creutzfeldt, B., Schroeder, S., Wziontek, H., 2017. Landscape-scale water balance monitoring with an iGrav superconducting gravimeter in a field enclosure. Hydrol. Earth Syst. Sci. 21 (6), 3167-3182. https://doi.org/10.5194/hess-21-3167-2017.

Guth, P.L., Van Niekerk, A., Grohmann, C.H., Muller, J.P., Hawker, L., Florinsky, I.V., Gesch, D., Reuter, H.I., Herrera-Cruz, V., Riazanoff, S., Lopez-Vazquez, C., Carabajal, C.C., Albinet, C., Strobl, P., 2021. Digital elevation models: terminology and definitions. Remote Sens. (Basel) 13 (18). https://doi.org/10.3390/rs13183581.

Haque, A., Salama, A., Lo, K., Wu, P., 2021. Surface and groundwater interactions: a review of coupling strategies in detailed domain models. Hydrology 8 (1), 35.

Hassan, S.M.T., Ghimire, C.P., Lubczynski, M.W., 2017. Remote sensing upscaling of interception loss from isolated oaks: sardon catchment case study, Spain. J. Hydrol. 555, 489-505. https://doi.org/10.1016/j.JHYDROL.2017.08.016.

Hassan, S.M.T., Lubczynski, M.W., 2024. Surface-groundwater interactions in hard rock, water-limited environments, simulated at very fine scale using long time-series observations and hydrotepe-modeling concept. J. Hydrol. 130505 https://doi.org/10.1016/j.jhydrol.2023.130505.

Hawker, L., Neal, J., Bates, P., 2019. Accuracy assessment of the TanDEM-X 90 digital elevation model for selected floodplain sites. Remote Sens. Environ. 232, 111319 https://doi.org/10.1016/j.rse.2019.111319.

Hawker, L., Uhe, P., Paulo, L., Sosa, J., Savage, J., Sampson, C., Neal, J., 2022. A 30 m global map of elevation with forests and buildings removed. Environ. Res. Lett. 17 (2) https://doi.org/10.1088/1748-9326/ac4d4f.

He, X., Lucatero, D., Ridler, M.E., Madsen, H., Kidmose, J., Hole, Ø., Petersen, C., Zheng, C., Refsgaard, J.C., 2019. Real-time simulation of surface water and groundwater with data assimilation. Adv. Water Resour. 127, 13-25. https://doi.org/10.1016/j.adwatres.2019.03.004.

Hector, B., Séguis, L., Hinderer, J., Descloitres, M., Vouillamoz, J.-M., Wubda, M., Boy, J.-P., Luck, B., Le Moigne, N., 2013. Gravity effect of joint storage changes in a weathered hard-rock aquifer in West Africa: results from joint absolute gravity, hydrological monitoring and geophysical prospecting. Geophys. J. Int. 194 (2), 737-750. https://doi.org/10.1093/gji/ggt146.

Herkenrath, D., Fiandaca, G., Auken, E., Bauer-Gottwein, P., 2013a. Sequential and joint hydrogeophysical inversion using a field-scale groundwater model with ERT and TDEM data. Hydrol. Earth Syst. Sci. 17 (10), 4043-4060. https://doi.org/10.5194/hess-17-4043-2013.

Herkenrath, D., Odlum, N., Nenna, V., Knight, R., Auken, E., Bauer-Gottwein, P., 2013b. Calibrating a salt water intrusion model with time-domain electromagnetic data. Ground Water 51 (3), 385-397. https://doi.org/10.1111/j.1745-6584.2012.00974.x.

Honarbakhsh, A., Tahmoures, M., Afzali, S.F., Khajehzadeh, M., Ali, M.S., 2022. Remote sensing and relief data to predict soil saturated hydraulic conductivity in a calcareous watershed. Iran. Catena 212. https://doi.org/10.1016/j.catena.2022.106046.

Houtz, D., Naderpour, R., Schwank, M., 2020a. A Cost-Effective Portable L-Band Radiometer for Drone and Ground-Based Applications. IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, pp. 6531-6534. DOI: 10.1109/IGARSS39084.2020.9324231.

Houtz, D., Naderpour, R., Schwank, M., 2020b. Portable L-band radiometer (PoLRa): design and characterization. Remote Sens. (Basel) 12 (17), 2780.

Hu, L.T., Jiao, J.J., 2015. Calibration of a large-scale groundwater flow model using GRACE data: a case study in the Qaidam Basin. China. Hydrogeology Journal 23 (7), 1305-1317. https://doi.org/10.1007/s10040-015-1278-6.

Huang, Q., Long, D., Han, Z.Y., Han, P.F., 2022. High-resolution satellite images combined with hydrological modeling derive river discharge for headwaters: a step toward discharge estimation in ungauged basins. Remote Sens. Environ. 277 https://doi.org/10.1016/j.rse.2022.113030.

Hübner, R., Heller, K., Günther, T., Kleber, A., 2015. Monitoring hillslope moisture dynamics with surface ERT for enhancing spatial significance of hydrometric point measurements. Hydrol. Earth Syst. Sci. 19 (1), 225-240. https://doi.org/10.5194/hess-19-225-2015.

Hughes, J.D., Langevin, C.D., Banta, E.R., 2017. Documentation for the MODFLOW 6 framework 6-A57, Reston, VA. DOI:10.3133/tm6A57.

Humphrey, V., Rodell, M., Eicker, A., 2023. Using satellite-based terrestrial water storage data: a review. Surv. Geophys. https://doi.org/10.1007/s10712-022-09754-9.

Immerzeel, W.W., Droogers, P., 2008. Calibration of a distributed hydrological model based on satellite evapotranspiration. J. Hydrol. 349 (3-4), 411-424. https://doi.org/10.1016/j.jhydrol.2007.11.017.

Jiang, L., Bauer-Gottwein, P., 2019. How do GPM IMERG precipitation estimates perform as hydrological model forcing? evaluation for 300 catchments across mainland China. J. Hydrol. 572, 486-500. https://doi.org/10.1016/j.jhydrol.2019.03.042.
Ezzy, T.R., Cox, M.E., O'Rourke, A.J., Huftile, G.J., 2006. Groundwater flow modelling within a coastal alluvial plain setting using a high-resolution hydrofacies approach; Bells Creek plain. Australia. Hydrogeology Journal 14 (5), 675-688. https://doi.org/10.1007/s10040-005-0470-5.

Finger, D., Vis, M., Huss, M., Seibert, J., 2015. The value of multiple data set calibration versus model complexity for improving the performance of hydrological models in mountain catchments. Water Resour. Res. 51 (4), 1939-1958. https://doi.org/10.1002/2014wr015712.

Flinchum, B.A., Banks, E., Hatch, M., Batelaen, O., Peeters, L.J.M., Pasquet, S., 2020. Identifying recharge under subtle ephemeral features in a flat-lying semi-arid region using a combined geophysical approach. Hydrol. Earth Syst. Sci. 24 (9), 4353-4368. https://doi.org/10.5194/hess-24-4353-2020.

Fores, B., Champollion, C., Le Moigne, N., Bayer, R., Chéry, J., 2016. Assessing the precision of the iGrav superconducting gravimeter for hydrological models and karstic hydrological process identification. Geophys. J. Int. 208 (1), 269-280. https://doi.org/10.1093/gji/ggw396.

Forte, E., Bondini, M.B., Bortoletto, A., Dossi, M., Colucci, R.R., 2019. Pros and cons in helicopter-borne GPR data acquisition on rugged mountainous areas: critical analysis and practical guidelines. Pure Appl. Geophys. 176 (10), 4533-4554. https://doi.org/10.1007/s00024-019-02196-2.

Foti, S., Comina, C., Boiero, D., Socco, L.V., 2009. Non-uniqueness in surface-wave inversion and consequences on seismic site response analyses. Soil Dyn. Earthq. Eng. 29 (6), 982-993. https://doi.org/10.1016/j.soldyn.2008.11.004.

Francés, A.P., Lubczynski, M.W., 2011. Topsoil thickness prediction at the catchment scale by integration of invasive sampling, surface geophysics, remote sensing and statistical modeling. J. Hydrol. 405 (1), 31-47. https://doi.org/10.1016/j.jhydrol.2011.05.006.

Francés, A.P., Lubczynski, M.W., Roy, J., Santos, F.A.M., Mahmoudzadeh Ardekani, M. R., 2014. Hydrogeophysics and remote sensing for the design of hydrogeological conceptual models in hard rocks - sardón catchment (Spain). J. Appl. Geophys. 110, 63-81. https://doi.org/10.1016/j.JAPPGEO.2014.08.015.

Frances, A.P., Ramalho, E.C., Fernandes, J., Groen, M., Hugman, R., Khalil, M.A., De Plaan, J., Santos, F.A.M., 2015. Contributions of hydrogeophysics to the hydrogeological conceptual model of the Albufera-Ribeira de Quarteira coastal aquifer in Algarve. Portugal. Hydrogeology Journal 23 (7), 1553-1572. https://doi.org/10.1007/s10040-015-1282-x.

Frappart, F., Ramillien, G., 2018. Monitoring groundwater storage changes using the gravity recovery and climate experiment (GRACE). Satellite Mission: A Review. Remote Sensing 10 (6). https://doi.org/10.3390/rs10060829.

Gallistl, J., Schwindt, D., Birgit, J., Aigner, L., Pererson, M., Flores Orozco, A., 2022. Quantification of soil textural and hydraulic properties in a complex conductivity imaging framework: results from the wolfsberg slope. Front. Earth Sci. 10 https://doi.org/10.3389/feart.2022.911611.

Garambois, S., Visoin, C., Guzman, M.A.R., Brito, D., Guiller, B., Refloch, A., 2019. Analysis of ballistic waves in seismic noise monitoring of water table variations in a water field site: added value from numerical modelling to data understanding. Geophys. J. Int. 219 (3), 1636-1647. https://doi.org/10.1093/gji/ggz391.

Garkoti, A., Kundapura, S., 2021. Deriving water level data and discharge estimation using satellite altimetry for Krishna River. Karnataka. Remote Sensing Applications-Society and Environment 22. https://doi.org/10.1016/j.rsse.2021.100487.

Gash, J.H.C., Lloyd, C.R., Lachaud, G., 1995. Estimating sparse forest rainfall interception using an analytical model. J. Hydrol. 170 (1-4), 79-86. https://doi.org/10.1016/0022-1694(95)02697-N.

Gaur, S., Singh, B., Bandyopadhyay, A., Stisen, S., Singh, R., 2022. Spatial pattern-based performance evaluation and uncertainty analysis of a distributed hydrological model. Hydrol. Process. 36 (5) https://doi.org/10.1002/hyp.14586.

Gebremedhin, M.A., Lubczynski, M.W., Maathuis, B.P., Teka, D., 2021. Novel approach to integrate daily satellite rainfall with in-situ rainfall, upper Kekeze Basin. Ethiopia. Atmospheric Research 248. https://doi.org/10.1016/j.atmosres.2020.105135.

Gebremedhin, M.A., Lubczynski, M.W., Maathuis, B.H.P., Teka, D., 2022. Deriving potential evapotranspiration from satellite-based reference evapotranspiration, upper Kekeze Basin, northern Ethiopia. J. Hydrol.: Reg. Stud. 41 https://doi.org/10.1016/j.ejrh.2022.101059.

Gebremedhin, M.A., Lubczynski, M.W., Maathuis, B.H.P., Daoud, M.G., Teka, D., 2023. Spatio-temporal rainfall interception loss at the catchment scale from earth observation in a data-scarce area, northern Ethiopia. J. Hydrol. 626, 130126 https://doi.org/10.1016/j.jhydrol.2023.130126.

Gelsinari, S., Doble, R., Daly, E., Pauwels, V.R.N., 2020. Feasibility of improving groundwater modeling by assimilating evapotranspiration rates. Water Resour. Res. 56 (2) https://doi.org/10.1029/2019wr025983.

Gelsinari, S., Pauwels, V.R.N., Daly, E., van Dam, J., Uijlenhoet, R., Fewster-Young, N., Doble, R., 2021. Unsaturated zone model complexity for the assimilation of evapotranspiration rates in groundwater modelling. Hydrol. Earth Syst. Sci. 25 (4), 2261-2277. https://doi.org/10.5194/hess-25-2261-2021.

Gelsinari, S., Doody, T.M., Thompson, S.E., Doble, R., Daly, E., Pauwels, V.R.N., 2022. Informing hydrogeological models with remotely sensed evapotranspiration. Frontiers in Water 4. https://doi.org/10.3389/frwa.2022.932641.

Gerrits, A.M.J., Pfister, L., Savenije, H.H.G., 2010. Spatial and temporal variability of canopy and forest floor interception in a beech forest. Hydrol. Process. 24 (21), 3011-3025. https://doi.org/10.1002/hyp.7712.

Ghimire, C.P., Bruijnzeel, L.A., Lubczynski, M.W., Bonell, M., 2012. Rainfall interception by natural and planted forests in the Middle Mountains of Central Nepal. J. Hydrol. 475, 270-280. https://doi.org/10.1016/j.jhydrol.2012.09.051.

Ghimire, C.P., Adrian Bruijnzeel, L., Lubczynski, M.W., Ravelona, M., Zwartendijk, B.W., van Meerveld, H.J., 2017. Measurement and modeling of rainfall interception by two

differently aged secondary forests in upland eastern Madagascar. J. Hydrol. 545, 212-225. https://doi.org/10.1016/j.jhydrol.2016.10.032.

Ghimire, U., Srinivasan, G., Agarwal, A., 2019. Assessment of rainfall bias correction techniques for improved hydrological simulation. Int. J. Climatol. 39 (4), 2386-2399. https://doi.org/10.1002/joc.5959.

Gong, Y., Yang, K., Lin, Z., Fang, S., Wu, X., Zhu, R., Peng, Y., 2021. Remote estimation of leaf area index (LAI) with unmanned aerial vehicle (UAV) imaging for different rice cultivars throughout the entire growing season. Plant Methods 17 (1), 88. https://doi.org/10.1186/s13007-021-00789-4.

Grombacher, D., Liu, L., Griffiths, M.P., Vang, M.O., Larsen, J.J., 2021. Steady-state surface NMR for mapping of groundwater. Geophys. Res. Lett. 48 (23) https://doi.org/10.1029/2021gl095381.

Grombacher, D., Griffiths, M.P., Liu, L., Vang, M.O., Larsen, J.J., 2022. Frequency shifting steady-state surface NMR signals to avoid problematic narrowband-noise sources. Geophys. Res. Lett. 49 (7) https://doi.org/10.1029/2021gl097402.

Güntner, A., Reich, M., Mikolaj, M., Creutzfeldt, B., Schroeder, S., Wziontek, H., 2017. Landscape-scale water balance monitoring with an iGrav superconducting gravimeter in a field enclosure. Hydrol. Earth Syst. Sci. 21 (6), 3167-3182. https://doi.org/10.5194/hess-21-3167-2017.

Guth, P.L., Van Niekerk, A., Grohmann, C.H., Muller, J.P., Hawker, L., Florinsky, I.V., Gesch, D., Reuter, H.I., Herrera-Cruz, V., Riazanoff, S., Lopez-Vazquez, C., Carabajal, C.C., Albinet, C., Strobl, P., 2021. Digital elevation models: terminology and definitions. Remote Sens. (Basel) 13 (18). https://doi.org/10.3390/rs13183581.

Haque, A., Salama, A., Lo, K., Wu, P., 2021. Surface and groundwater interactions: a review of coupling strategies in detailed domain models. Hydrology 8 (1), 35.

Hassan, S.M.T., Ghimire, C.P., Lubczynski, M.W., 2017. Remote sensing upscaling of interception loss from isolated oaks: sardon catchment case study, Spain. J. Hydrol. 555, 489-505. https://doi.org/10.1016/j.JHYDROL.2017.08.016.

Hassan, S.M.T., Lubczynski, M.W., 2024. Surface-groundwater interactions in hard rock, water-limited environments, simulated at very fine scale using long time-series observations and hydrotepe-modeling concept. J. Hydrol. 130505 https://doi.org/10.1016/j.jhydrol.2023.130505.

Hawker, L., Neal, J., Bates, P., 2019. Accuracy assessment of the TanDEM-X 90 digital elevation model for selected floodplain sites. Remote Sens. Environ. 232, 111319 https://doi.org/10.1016/j.rse.2019.111319.

Hawker, L., Uhe, P., Paulo, L., Sosa, J., Savage, J., Sampson, C., Neal, J., 2022. A 30 m global map of elevation with forests and buildings removed. Environ. Res. Lett. 17 (2) https://doi.org/10.1088/1748-9326/ac4d4f.

He, X., Lucatero, D., Ridler, M.E., Madsen, H., Kidmose, J., Hole, Ø., Petersen, C., Zheng, C., Refsgaard, J.C., 2019. Real-time simulation of surface water and groundwater with data assimilation. Adv. Water Resour. 127, 13-25. https://doi.org/10.1016/j.adwatres.2019.03.004.

Hector, B., Séguis, L., Hinderer, J., Descloitres, M., Vouillamoz, J.-M., Wubda, M., Boy, J.-P., Luck, B., Le Moigne, N., 2013. Gravity effect of joint storage changes in a weathered hard-rock aquifer in West Africa: results from joint absolute gravity, hydrological monitoring and geophysical prospection. Geophys. J. Int. 194 (2), 737-750. https://doi.org/10.1093/gji/ggt146.

Herkenrath, D., Fiandaca, G., Auken, E., Bauer-Gottwein, P., 2013a. Sequential and joint hydrogeophysical inversion using a field-scale groundwater model with ERT and TDEM data. Hydrol. Earth Syst. Sci. 17 (10), 4043-4060. https://doi.org/10.5194/hess-17-4043-2013.

Herkenrath, D., Odlum, N., Nenna, V., Knight, R., Auken, E., Bauer-Gottwein, P., 2013b. Calibrating a salt water intrusion model with time-domain electromagnetic data. Ground Water 51 (3), 385-397. https://doi.org/10.1111/j.1745-6584.2012.00974.x.

Honarbakhsh, A., Tahmoures, M., Afzali, S.F., Khajehzadeh, M., Ali, M.S., 2022. Remote sensing and relief data to predict soil saturated hydraulic conductivity in a calcareous watershed. Iran. Catena 212. https://doi.org/10.1016/j.catena.2022.106046.

Houtz, D., Naderpour, R., Schwank, M., 2020a. A Cost-Effective Portable L-Band Radiometer for Drone and Ground-Based Applications. IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, pp. 6531-6534. DOI: 10.1109/IGARSS39084.2020.9324231.

Houtz, D., Naderpour, R., Schwank, M., 2020b. Portable L-band radiometer (PoLRa): design and characterization. Remote Sens. (Basel) 12 (17), 2780.

Hu, L.T., Jiao, J.J., 2015. Calibration of a large-scale groundwater flow model using GRACE data: a case study in the Qaidam Basin. China. Hydrogeology Journal 23 (7), 1305-1317. https://doi.org/10.1007/s10040-015-1278-6.

Huang, Q., Long, D., Han, Z.Y., Han, P.F., 2022. High-resolution satellite images combined with hydrological modeling derive river discharge for headwaters: a step toward discharge estimation in ungauged basins. Remote Sens. Environ. 277 https://doi.org/10.1016/j.rse.2022.113030.

Hübner, R., Heller, K., Günther, T., Kleber, A., 2015. Monitoring hillslope moisture dynamics with surface ERT for enhancing spatial significance of hydrometric point measurements. Hydrol. Earth Syst. Sci. 19 (1), 225-240. https://doi.org/10.5194/hess-19-225-2015.

Hughes, J.D., Langevin, C.D., Banta, E.R., 2017. Documentation for the MODFLOW 6 framework 6-A57, Reston, VA. DOI:10.3133/tm6A57.

Humphrey, V., Rodell, M., Eicker, A., 2023. Using satellite-based terrestrial water storage data: a review. Surv. Geophys. https://doi.org/10.1007/s10712-022-09754-9.

Immerzeel, W.W., Droogers, P., 2008. Calibration of a distributed hydrological model based on satellite evapotranspiration. J. Hydrol. 349 (3-4), 411-424. https://doi.org/10.1016/j.jhydrol.2007.11.017.

Jiang, L., Bauer-Gottwein, P., 2019. How do GPM IMERG precipitation estimates perform as hydrological model forcing? evaluation for 300 catchments across mainland China. J. Hydrol. 572, 486-500. https://doi.org/10.1016/j.jhydrol.2019.03.042.
Jiang, L.G., Christensen, S.W., Bauer-Gottwein, P., 2021. Calibrating 1D hydrodynamic river models in the absence of cross-section geometry using satellite observations of water surface elevation and river width. Hydrol. Earth Syst. Sci. 25 (12), 6359–6379. https://doi.org/10.5194/hess-25-6359-2021.

Jiang, R., Wang, P., Xu, Y., Zhou, Z., Luo, X., Lan, Y., Zhao, G., Sanchez-Azofeifa, A., Laakso, K., 2020. Assessing the operation parameters of a low-altitude UAV for the collection of NDVI values over a Paddy Rice field. Remote Sens. (Basel) 12 (11), 1850.

Jyolsna, P.J., Kambhammettu, B., Gorugantula, S., 2021. Application of random forest and multi-linear regression methods in downscaling GRACE derived groundwater storage changes. Hydrological Sciences Journal-Diurnal Des Sciences Hydrologiques 66 (5), 874–887. https://doi.org/10.1080/02626677.2021.1896719.

Karaoulis, M., Ritsema, I., Bremmer, C., De Kleine, M., Essink, G.O., Ahlrichs, E., 2022. Drone-borne electromagnetic (DR-EM) surveying in the Netherlands: lab and field validation results. Remote Sens. (Basel) 14 (21). https://doi.org/10.3390/rs14215335.

Khaki, M., Hendricks Franssen, H.J., Han, S.C., 2020. Multi-mission satellite remote sensing data for improving land hydrological models via data assimilation. Scientific Reports 2020 10:1, 10(1): 1-23. DOI:10.1038/s41598-020-75710-5.

Kidd, C., Becker, A., Huffman, G.J., Muller, C.L., Joe, P., Skofronick-Jackson, G., Kirschbaum, D.B., 2017. So, how much of the earth's surface is covered by rain gauges? Bull Am Meteorol Soc 98 (1), 69–78. https://doi.org/10.1175/bams-d-14-00283.1.

Kittel, C.M.M., Jiang, L.G., Totrup, C., Bauer-Gottwein, P., 2021. Sentinel-3 radar altimetry for river monitoring - a catchment-scale evaluation of satellite water surface elevation from sentinel-3A and sentinel-3B. Hydrol. Earth Syst. Sci. 25 (1), 333–357. https://doi.org/10.5194/hess-25-333-2021.

Kollet, S.J., Maxwell, R.M., 2006. Integrated surface-groundwater flow modeling: a free-surface overland flow boundary condition in a parallel groundwater flow model. Adv. Water Resour. 29 (7), 945–958. https://doi.org/10.1016/j.adwatres.2005.08.006.

Lambot, S., Slob, E., Chavarro, D., Lubczynski, M., Vereecken, H., 2008. Measuring soil surface water content in irrigated areas of southern Tunisia using full-waveform inversion of proximal GPR data. Near Surf. Geophys. 6 (6), 403–410.

Langevin, C.D., Hughes, J.D., Banta, E.R., Niswonger, R.G., Panday, S., Provost, A.M., 2017. Documentation for the MODFLOW t6 Groundwater Flow Model. DOI:10.3133/t6ma655.

Larsen, J.J., Langhof, R.B., Kjaer, M.W., Vang, M., Liu, L.C., Griffiths, M., Grombacher, D., 2021. Efficient processing of surface NMR data with spectral analysis. Geophys. J. Int. 229 (1), 286–298. https://doi.org/10.1093/gji/ggab472.

Le Coz, C., van de Giesen, N., 2020. Comparison of rainfall products over sub-saharan Africa. J. Hydrometeorol. 21 (4), 553–596. https://doi.org/10.1175/jhm-d-18-02561.

Leblanc, M., Favreau, G., Maley, J., Nazoumou, Y., Leduc, C., Stagnitti, F., van Oevelen, P.J., Delclaux, F., Lemoalle, J., 2006a. Reconstruction of megalake Chad using shuttle radar topographic Mission data. Palaeogeogr. Palaeoclimatol. Palaeoecol. 239 (1–2), 16–27. https://doi.org/10.1016/j.paleo.2006.01.003.

Leblanc, M., Favreau, G., Tweed, S., Leduc, C., Razack, M., Mofor, L., 2007. Remote sensing for groundwater modelling in large semiarid areas: Lake Chad Basin. Africa. Hydrogeology Journal 15 (1), 97–100. https://doi.org/10.1007/s10040-006-0126-0.

Leblanc, M.J., Leduc, C., Stagnitti, F., van Oevelen, P.J., Jones, C., Mofor, L.A., Razack, M., Favreau, G., 2006b. Evidence for megalake Chad, north-Central Africa, during the late Quaternary from satellite data. Palaeogeogr. Palaeoclimatol. Palaeoecol. 230 (3–4), 230–242. https://doi.org/10.1016/j.palaeo.2005.07.016.

Leblanc, M.J., Favreau, G., Massuel, S., Tweed, S.O., Loireau, M., Cappelaere, B., 2008. Land clearance and hydrological change in the Sahel: SW Niger. Global Planet. Change 61 (3–4), 135–150. https://doi.org/10.1016/j.gloplacha.2007.08.011.

Leblanc, M., Lemoalle, J., Bader, J.C., Tweed, S., Mofor, L., 2012. Thermal remote sensing of water under flooded vegetation: new observations of inundation patterns for the 'small' Lake Chad. J. Hydrol. 404 (1), 87–98. https://doi.org/10.1016/j.jhydrol.2011.04.023.

Legchenko, A., Ezersky, M., Camerlynck, C., Al-Zoubi, A., Chalikakis, K., Girard, J.-F., 2008. Locating water-filled karst caverns and estimating their volume using magnetic resonance soundings. Geophysics 73 (5), G51–G61. https://doi.org/10.1190/1.2958007.

Legchenko, A., Vouillamoz, J.M., Lawson, F.M.A., Alle, C., Desclaitres, M., Boucher, M., 2016. Interpretation of magnetic resonance measurements in the varying earth's magnetic field. Geophysics 81 (4), WB23-WB31. https://doi.org/10.1190/geo2015-04741.

Legchenko, A., Baltassat, J.M., Duwig, C., Boucher, M., Girard, J.F., Soruco, A., Beauce, A., Mathieu, F., Legout, C., Desclaitres, M., Patricia, F.A.G., 2020. Time-lapse magnetic resonance sounding measurements for numerical modeling of water flow in variably saturated media. J. Appl. Geophys. 175 https://doi.org/10.1016/j.jappgeo.2020.103984.

Legchenko, A., Jean-Michel, B., Mohamad, A., Arnaud, I., Nadia, A., Mohamed, A., Jacques, D., Clemence, R., Pauline, K., 2022. Monitoring unsaturated water flow using magnetic resonance soundings. J. Hydrol. 612, 128318 https://doi.org/10.1016/j.jhydrol.2022.128318.

Lekula, M., Lubczynski, M.W., Shemang, E.M., Verhoef, W., 2018. Validation of satellite-based rainfall in kalahari. Phys. Chem. Earth 105, 84–97. https://doi.org/10.1016/j.pce.2018.02.010.

Lesparre, N., Girard, J.F., Jeannot, B., Weill, S., Dumont, M., Boucher, M., Viville, D., Pierret, M.C., Legchenko, A., Delay, F., 2020. Magnetic resonance sounding measurements as posterior information to condition hydrological model parameters: application to a hard-rock headwater catchment. J. Hydrol. 587 https://doi.org/10.1016/j.jhydrol.2020.12491.

Levizzani, V., Cattani, E., 2019. Satellite remote sensing of precipitation and the terrestrial water cycle in a changing climate. Remote Sens. (Basel) 11 (19). https://doi.org/10.3390/rs11192301.

Li, B.L., Rodell, M., Kumar, S., Beaudoing, H.K., Getirana, A., Zaitchik, B.F., de Goncalves, L.G., Cossetin, C., Bhanja, S., Mukherjee, A., Tian, S.Y., Tangdamrongsub, N., Long, D., Nanteza, J., Lee, J., Policelli, F., Goni, I.B., Daira, D., Bila, M., de Lannoy, G., Mocko, D., Steele-Dunne, S.C., Save, H., Bettadpur, S., 2019. GRACE data assimilation for groundwater and drought monitoring: advances and challenges. Water Resour. Res. 55 (9), 7564–7586. https://doi.org/10.1029/2018wr024618.

Li, M.N., Zeng, Y.J., Lubczynski, M.W., Roy, J., Yu, L.Y., Qian, H., Li, Z.Y., Chen, J., Han, L., Zheng, H., Veldkamp, T., Schoorl, J.M., Franssen, H.J.H., Hou, K., Zhang, Q., Yu, P.P., Li, F., Lu, K., Li, Y.L., Su, Z.B., 2021. A first investigation of hydrogeology and hydrogeophysics of the maqu catchment in the Yellow River source region. Earth Syst. Sci. Data 13 (10), 4727–4757. https://doi.org/10.5194/esd-13-4727-2021.

Liu, J., Bauer-Gottwein, P., Coppo Frias, M., Musaeus, A., Christoffersen, L., Jiang, L., 2023. Stage-slope-discharge relationships upstream of river confluences revealed by satellite altimetry. Geophys. Res. Lett. 50 https://doi.org/10.1029/2023GL063694.

Liu, C., Hu, R., Wang, Y., Lin, H., Zeng, H., Wu, D., Liu, Z., Dai, Y., Song, X., Shao, C., 2022. Monitoring water level and volume changes of lakes and reservoirs in the Yellow River Basin using ICESat-2 laser altimetry and Google earth engine. J. Hydro Environ. Res. 44, 53–64. https://doi.org/10.1016/j.jher.2022.07.005.

Liu, Y., Weerts, A.H., Clark, M., Franssen, H.J.H., Kumar, S., Moradkhani, H., Seo, D.J., Schwansenberg, D., Smith, P., van Dijk, A., van Velzen, N., He, M., Lee, H., Noh, S.J., Rakovec, O., Restrepo, P., 2012. Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities. Hydrol. Earth Syst. Sci. 16 (10), 3863–3887. https://doi.org/10.5194/hess-16-3863-2012.

Lopez, P.L., Sutanudjaja, E.H., Schellekens, J., Sterk, G., Bierkens, M.F.P., 2017. Calibration of a large-scale hydrological model using satellite-based soil moisture and evapotranspiration products. Hydrol. Earth Syst. Sci. 21 (6), 3125–3144. https://doi.org/10.5194/hess-21-3125-2017.

Lu, Q., Si, W., Wei, L., Li, Z., Xia, Z., Ye, S., Xia, Y., 2021. Retrieval of water quality from UAV-borne hyperspectral imagery: a comparative study of machine learning algorithms. Remote Sens. (Basel) 13 (19), 3928.

Lubczynski, M., Roy, J., 2003. Hydrogeological interpretation and potential of the new magnetic resonance sounding (MRS) method. J. Hydrol. 283 (1–4), 19–40. https://doi.org/10.1016/s0022-1694(03)00170-7.

Lubczynski, M., Roy, J., 2004. Magnetic resonance sounding: new method for ground water assessment. Ground Water 42 (2), 291–303.

Lubczynski, M., Roy, J., 2005. MRS contribution to hydrogeological system parametrization. Near Surf. Geophys. 3 (3), 131–139.

Lubczynski, M.W., Roy, J., 2007. Use of MRS for hydrogeological system parameterization and modelling. Bol. Geol. Min. 118(2007) 3, 509–530.

Luoma, S., Majaniemi, J., Pullinen, A., Mursu, J., Virtasalo, J.J., 2021. Geological and groundwater flow model of a submarine groundwater discharge site at Hanko (Finland), northern Baltic Sea. Hydrol. J. 29 (3), 1279–1297. https://doi.org/10.1007/s10040-021-02313-3.

Maathuis, B.H.P., Mannaerts, C.M., Schouwenburg, M.L., Retsios, V., Lemmens, R., Moithobogi, T., 2012. Geonetcast toolbox: installation, configuration and user guide of the Geonetcast Toolbox plug - in for ILWIS 3.7.2: XML version 1.3: e-book. University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC), Enschede.

Mahmoudzadeh, M.R., Frances, A., Lubczynski, M., Lambot, S., 2012. Using ground培育d radar to investigate the water table depth in weathered granites - sardon case study. Spain. J. Appl. Geophys. 79, 17–26. https://doi.org/10.1016/j.jappgeo.2011.12.009.

Mangel, A.R., Dawson, C.B., Rey, D.M., Briggs, M.A., 2022. Drone applications in hydrogeophysics: recent examples and a vision for the future. Lead. Edge 41 (8), 540–547. https://doi.org/10.1190/tle41080540.1.

Mano, K., Sakai, K., Tachibana, K., Sakita, K., Nishiyama, S., 2020. THE MEASUREMENT ACCURACY AND MEASUREMENT CHARACTERISTICS OF GREEN LIDAR DRONE. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B1-2020: 479-483. DOI: 10.5194/isprs-archives-XLIII-B1-2020-479-2020.

Marker, P.A., Foged, N., He, X., Christiansen, A.V., Refsgaard, J.C., Auken, E., Bauer-Gottwein, P., 2015. Performance evaluation of groundwater model hydrostratigraphy from airborne electromagnetic data and lithological borehole logs. Hydrol. Earth Syst. Sci. 19 (9), 3875–3890. https://doi.org/10.5194/hess-19-3875-2015.

Markstrom, S.L., Niswonger, R.G., Regan, R.S., Prudic, D.E., Barlow, P.M., 2008. GSFLOW - Coupled Ground-Water and Surface-Water Flow Model Based on the Integration of the Precipitation-Runoff Modeling System (PRMS) and the Modular Ground-Water Flow Model (MODFLOW-2005). DOI:10.3133/TM6D1.

Martens, B., Miralles, D.G., Lievens, H., van der Schalie, R., de Jeu, R.A.M., Fernández-Prieto, D., Beck, H.E., Dorigo, W.A., Verhoest, N.E.C., 2017. GLEAM v3: satellite-based land evaporation and root-zone soil moisture. Geosci. Model Dev. 10 (5), 1903–1925. https://doi.org/10.5194/gmd-10-1903-2017.

Marzahn, P., Meyer, S., 2020. Utilization of multi-temporal microwave remote sensing data within a geostatistical regionalization approach for the derivation of soil texture. Remote Sens. (Basel) 12 (16), 2660.

Maxwell, R.M., Putti, M., Meyerhoff, S., Delfs, J.O., Ferguson, I.M., Ivanov, V., Kim, J., Kolditz, O., Kollet, S.J., Kumar, M., Lopez, S., Niu, J., Paniconi, C., Park, Y.J., Phanikumar, M.S., Shen, C., Sudicky, E.A., Sulis, M., 2014. Surface-subsurface model intercomparison: a first set of benchmark results to diagnose integrated hydrology application to a hard-rock headwater catchment. J. Hydrol. 587 https://doi.org/10.1016/j.jhydrol.2020.12491.
Jiang, L.G., Christensen, S.W., Bauer-Gottwein, P., 2021. Calibrating 1D hydrodynamic river models in the absence of cross-section geometry using satellite observations of water surface elevation and river width. Hydrol. Earth Syst. Sci. 25 (12), 6359–6379. https://doi.org/10.5194/hess-25-6359-2021.

Jiang, R., Wang, P., Xu, Y., Zhou, Z., Luo, X., Lan, Y., Zhao, G., Sanchez-Azofeifa, A., Laakso, K., 2020. Assessing the operation parameters of a low-altitude UAV for the collection of NDVI values over a Paddy Rice field. Remote Sens. (Basel) 12 (11), 1850.

Jyolsna, P.J., Kambhammettu, B., Gorugantula, S., 2021. Application of random forest and multi-linear regression methods in downscaling GRACE derived groundwater storage changes. Hydrological Sciences Journal-Diurnal Des Sciences Hydrologiques 66 (5), 874–887. https://doi.org/10.1080/02626677.2021.1896719.

Karaoulis, M., Ritsema, I., Bremmer, C., De Kleine, M., Essink, G.O., Ahlrichs, E., 2022. Drone-borne electromagnetic (DR-EM) surveying in the Netherlands: lab and field validation results. Remote Sens. (Basel) 14 (21). https://doi.org/10.3390/rs14215335.

Khaki, M., Hendricks Franssen, H.J., Han, S.C., 2020. Multi-mission satellite remote sensing data for improving land hydrological models via data assimilation. Scientific Reports 2020 10:1, 10(1): 1-23. DOI:10.1038/s41598-020-75710-5.

Kidd, C., Becker, A., Huffman, G.J., Muller, C.L., Joe, P., Skofronick-Jackson, G., Kirschbaum, D.B., 2017. So, how much of the earth's surface is covered by rain gauges? Bull Am Meteorol Soc 98 (1), 69–78. https://doi.org/10.1175/bams-d-14-00283.1.

Kittel, C.M.M., Jiang, L.G., Totrup, C., Bauer-Gottwein, P., 2021. Sentinel-3 radar altimetry for river monitoring - a catchment-scale evaluation of satellite water surface elevation from sentinel-3A and sentinel-3B. Hydrol. Earth Syst. Sci. 25 (1), 333–357. https://doi.org/10.5194/hess-25-333-2021.

Kollet, S.J., Maxwell, R.M., 2006. Integrated surface-groundwater flow modeling: a free-surface overland flow boundary condition in a parallel groundwater flow model. Adv. Water Resour. 29 (7), 945–958. https://doi.org/10.1016/j.adwatres.2005.08.006.

Lambot, S., Slob, E., Chavarro, D., Lubczynski, M., Vereecken, H., 2008. Measuring soil surface water content in irrigated areas of southern Tunisia using full-waveform inversion of proximal GPR data. Near Surf. Geophys. 6 (6), 403–410.

Langevin, C.D., Hughes, J.D., Banta, E.R., Niswonger, R.G., Panday, S., Provost, A.M., 2017. Documentation for the MODFLOW t6 Groundwater Flow Model. DOI:10.3133/t6ma655.

Larsen, J.J., Langhof, R.B., Kjaer, M.W., Vang, M., Liu, L.C., Griffiths, M., Grombacher, D., 2021. Efficient processing of surface NMR data with spectral analysis. Geophys. J. Int. 229 (1), 286–298. https://doi.org/10.1093/gji/ggab472.

Le Coz, C., van de Giesen, N., 2020. Comparison of rainfall products over sub-saharan Africa. J. Hydrometeorol. 21 (4), 553–596. https://doi.org/10.1175/jhm-d-18-02561.

Leblanc, M., Favreau, G., Maley, J., Nazoumou, Y., Leduc, C., Stagnitti, F., van Oevelen, P.J., Delclaux, F., Lemoalle, J., 2006a. Reconstruction of megalake Chad using shuttle radar topographic Mission data. Palaeogeogr. Palaeoclimatol. Palaeoecol. 239 (1–2), 16–27. https://doi.org/10.1016/j.palaeo.2006.01.003.

Leblanc, M., Favreau, G., Tweed, S., Leduc, C., Razack, M., Mofor, L., 2007. Remote sensing for groundwater modelling in large semiarid areas: Lake Chad Basin. Africa. Hydrogeology Journal 15 (1), 97–100. https://doi.org/10.1007/s10040-006-0126-0.

Leblanc, M.J., Leduc, C., Stagnitti, F., van Oevelen, P.J., Jones, C., Mofor, L.A., Razack, M., Favreau, G., 2006b. Evidence for megalake Chad, north-Central Africa, during the late Quaternary from satellite data. Palaeogeogr. Palaeoclimatol. Palaeoecol. 230 (3–4), 230–242. https://doi.org/10.1016/j.palaeo.2005.07.016.

Leblanc, M.J., Favreau, G., Massuel, S., Tweed, S.O., Loireau, M., Cappelaere, B., 2008. Land clearance and hydrological change in the Sahel: SW Niger. Global Planet. Change 61 (3–4), 135–150. https://doi.org/10.1016/j.gloplacha.2007.08.011.

Leblanc, M., Lemoalle, J., Bader, J.C., Tweed, S., Mofor, L., 2012. Thermal remote sensing of water under flooded vegetation: new observations of inundation patterns for the 'small' Lake Chad. J. Hydrol. 404 (1), 87–98. https://doi.org/10.1016/j.jhydrol.2011.04.023.

Legchenko, A., Ezersky, M., Camerlynck, C., Al-Zoubi, A., Chalikakis, K., Girard, J.-F., 2008. Locating water-filled karst caverns and estimating their volume using magnetic resonance soundings. Geophysics 73 (5), G51–G61. https://doi.org/10.1190/1.2958007.

Legchenko, A., Vouillamoz, J.M., Lawson, F.M.A., Alle, C., Desclaitres, M., Boucher, M., 2016. Interpretation of magnetic resonance measurements in the varying earth's magnetic field. Geophysics 81 (4), WB23-WB31. https://doi.org/10.1190/geo2015-04741.

Legchenko, A., Baltassat, J.M., Duwig, C., Boucher, M., Girard, J.F., Soruco, A., Beauce, A., Mathieu, F., Legout, C., Desclaitres, M., Patricia, F.A.G., 2020. Time-lapse magnetic resonance sounding measurements for numerical modeling of water flow in variably saturated media. J. Appl. Geophys. 175 https://doi.org/10.1016/j.jappgeo.2020.103984.

Legchenko, A., Jean-Michel, B., Mohamad, A., Arnaud, I., Nadia, A., Mohamed, A., Jacques, D., Clemence, R., Pauline, K., 2022. Monitoring unsaturated water flow using magnetic resonance soundings. J. Hydrol. 612, 128318 https://doi.org/10.1016/j.jhydrol.2022.128318.

Lekula, M., Lubczynski, M.W., Shemang, E.M., Verhoef, W., 2018. Validation of satellite-based rainfall in kalahari. Phys. Chem. Earth 105, 84–97. https://doi.org/10.1016/j.pce.2018.02.010.

Lesparre, N., Girard, J.F., Jeannot, B., Weill, S., Dumont, M., Boucher, M., Viville, D., Pierret, M.C., Legchenko, A., Delay, F., 2020. Magnetic resonance sounding measurements as posterior information to condition hydrological model parameters: application to a hard-rock headwater catchment. J. Hydrol. 587 https://doi.org/10.1016/j.jhydrol.2020.124941.

Levizzani, V., Cattani, E., 2019. Satellite remote sensing of precipitation and the terrestrial water cycle in a changing climate. Remote Sens. (Basel) 11 (19). https://doi.org/10.3390/rs11192301.

Li, B.L., Rodell, M., Kumar, S., Beaudoing, H.K., Getirana, A., Zaitchik, B.F., de Goncalves, L.G., Cossetin, C., Bhanja, S., Mukherjee, A., Tian, S.Y., Tangdamrongsub, N., Long, D., Nanteza, J., Lee, J., Policelli, F., Goni, I.B., Daira, D., Bila, M., de Lannoy, G., Mocko, D., Steele-Dunne, S.C., Save, H., Bettadpur, S., 2019. GRACE data assimilation for groundwater and drought monitoring: advances and challenges. Water Resour. Res. 55 (9), 7564–7586. https://doi.org/10.1029/2018wr024618.

Li, M.N., Zeng, Y.J., Lubczynski, M.W., Roy, J., Yu, L.Y., Qian, H., Li, Z.Y., Chen, J., Han, L., Zheng, H., Veldkamp, T., Schoorl, J.M., Franssen, H.J.H., Hou, K., Zhang, Q., Yu, P.P., Li, F., Lu, K., Li, Y.L., Su, Z.B., 2021. A first investigation of hydrogeology and hydrogeophysics of the maqu catchment in the Yellow River source region. Earth Syst. Sci. Data 13 (10), 4727–4757. https://doi.org/10.5194/esd-13-4727-2021.

Liu, J., Bauer-Gottwein, P., Coppo Frias, M., Musaeus, A., Christoffersen, L., Jiang, L., 2023. Stage-slope-discharge relationships upstream of river confluences revealed by satellite altimetry. Geophys. Res. Lett. 50 https://doi.org/10.1029/2023GL063694.

Liu, C., Hu, R., Wang, Y., Lin, H., Zeng, H., Wu, D., Liu, Z., Dai, Y., Song, X., Shao, C., 2022. Monitoring water level and volume changes of lakes and reservoirs in the Yellow River Basin using ICESat-2 laser altimetry and Google earth engine. J. Hydro Environ. Res. 44, 53–64. https://doi.org/10.1016/j.jher.2022.07.005.

Liu, Y., Weerts, A.H., Clark, M., Franssen, H.J.H., Kumar, S., Moradkhani, H., Seo, D.J., Schwansenberg, D., Smith, P., van Dijk, A., van Velzen, N., He, M., Lee, H., Noh, S.J., Rakovec, O., Restrepo, P., 2012. Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities. Hydrol. Earth Syst. Sci. 16 (10), 3863–3887. https://doi.org/10.5194/hess-16-3863-2012.

Lopez, P.L., Sutanudjaja, E.H., Schellekens, J., Sterk, G., Bierkens, M.F.P., 2017. Calibration of a large-scale hydrological model using satellite-based soil moisture and evapotranspiration products. Hydrol. Earth Syst. Sci. 21 (6), 3125–3144. https://doi.org/10.5194/hess-21-3125-2017.

Lu, Q., Si, W., Wei, L., Li, Z., Xia, Z., Ye, S., Xia, Y., 2021. Retrieval of water quality from UAV-borne hyperspectral imagery: a comparative study of machine learning algorithms. Remote Sens. (Basel) 13 (19), 3928.

Lubczynski, M., Roy, J., 2003. Hydrogeological interpretation and potential of the new magnetic resonance sounding (MRS) method. J. Hydrol. 283 (1–4), 19–40. https://doi.org/10.1016/s0022-1694(03)00170-7.

Lubczynski, M., Roy, J., 2004. Magnetic resonance sounding: new method for ground water assessment. Ground Water 42 (2), 291–303.

Lubczynski, M., Roy, J., 2005. MRS contribution to hydrogeological system parametrization. Near Surf. Geophys. 3 (3), 131–139.

Lubczynski, M.W., Roy, J., 2007. Use of MRS for hydrogeological system parameterization and modelling. Bol. Geol. Min. 118(2007) 3, 509–530.

Luoma, S., Majaniemi, J., Pullinen, A., Mursu, J., Virtasalo, J.J., 2021. Geological and groundwater flow model of a submarine groundwater discharge site at Hanko (Finland), northern Baltic Sea. Hydrol. J. 29 (3), 1279–1297. https://doi.org/10.1007/s10040-021-02313-3.

Maathuis, B.H.P., Mannaerts, C.M., Schouwenburg, M.L., Retsios, V., Lemmens, R., Moithobogi, T., 2012. Geonetcast toolbox: installation, configuration and user guide of the Geonetcast Toolbox plug - in for ILWIS 3.7.2: XML version 1.3: e-book. University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC), Enschede.

Mahmoudzadeh, M.R., Frances, A., Lubczynski, M., Lambot, S., 2012. Using ground penetrating radar to investigate the water table depth in weathered granites - sardon case study. Spain. J. Appl. Geophys. 79, 17–26. https://doi.org/10.1016/j.jappgeo.2011.12.009.

Mangel, A.R., Dawson, C.B., Rey, D.M., Briggs, M.A., 2022. Drone applications in hydrogeophysics: recent examples and a vision for the future. Lead. Edge 41 (8), 540–547. https://doi.org/10.1190/tle41080540.1.

Mano, K., Sakai, K., Tachibana, K., Sakita, K., Nishiyama, S., 2020. THE MEASUREMENT ACCURACY AND MEASUREMENT CHARACTERISTICS OF GREEN LIDAR DRONE. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B1-2020: 479-483. DOI: 10.5194/isprams-archives-LXIII-B1-2020-479-2020.

Marker, P.A., Foged, N., He, X., Christiansen, A.V., Refsgaard, J.C., Auken, E., Bauer-Gottwein, P., 2015. Performance evaluation of groundwater model hydrostratigraphy from airborne electromagnetic data and lithological borehole logs. Hydrol. Earth Syst. Sci. 19 (9), 3875–3890. https://doi.org/10.5194/hess-19-3875-2015.

Markstrom, S.L., Niswonger, R.G., Regan, R.S., Prudic, D.E., Barlow, P.M., 2008. GSFLOW - Coupled Ground-Water and Surface-Water Flow Model Based on the Integration of the Precipitation-Runoff Modeling System (PRMS) and the Modular Ground-Water Flow Model (MODFLOW-2005). DOI:10.3133/TM6D1.

Martens, B., Miralles, D.G., Lievens, H., van der Schalie, R., de Jeu, R.A.M., Fernández-Prieto, D., Beck, H.E., Dorigo, W.A., Verhoest, N.E.C., 2017. GLEAM v3: satellite-based land evaporation and root-zone soil moisture. Geosci. Model Dev. 10 (5), 1903–1925. https://doi.org/10.5194/gmd-10-1903-2017.

Marzahn, P., Meyer, S., 2020. Utilization of multi-temporal microwave remote sensing data within a geostatistical regionalization approach for the derivation of soil texture. Remote Sens. (Basel) 12 (16), 2660.

Maxwell, R.M., Putti, M., Meyerhoff, S., Delfs, J.O., Ferguson, I.M., Ivanov, V., Kim, J., Kolditz, O., Kollet, S.J., Kumar, M., Lopez, S., Niu, J., Paniconi, C., Park, Y.J., Phanikumar, M.S., Shen, C., Sudicky, E.A., Sulis, M., 2014. Surface-subsurface model intercomparison: a first set of benchmark results to diagnose integrated hydrology application to a hard-rock headwater catchment. J. Hydrol. 587 https://doi.org/10.1016/j.jhydrol.2020.124941.
and feedbacks. Water Resour. Res. 50 (2), 1531–1549. https://doi.org/10.1002/2013WR013725.

Maxwell, R.M., Condon, L.E., Kollet, S.J., 2015. A high-resolution simulation of groundwater and surface water over most of the continental US with the integrated hydrologic model ParFlow v3. Geosci. Model Dev. 8 (3), 923–937. https://doi.org/10.5194/gmd-8-923-2015.

McJanlett, D., Wallace, J., Reddell, P., 2007. Precipitation interception in australian tropical rainforests: II. altitudinal gradients of cloud interception, stemflow, throughfall and interception. Hydrol. Process. 21 (13), 1703–1718. https://doi.org/10.1002/hyp.6346.

McMillan, W.D., Burgy, R.H., 1960. Interception loss from grass. Journal of Geophysical Research (1896-1977), 65(8): 2389-2394. DOI:https://doi.org/10.1029/JZ065i008p02389.

Milzow, C., Krogh, P.E., Bauer-Gottwein, P., 2011. Combining satellite radar altimetry, SAR surface soil moisture and GRACE total storage changes for hydrological model calibration in a large poorly gauged catchment. Hydrol. Earth Syst. Sci. 15 (6), 1729–1743. https://doi.org/10.5194/hess-15-1729-2011.

Miralles, D.G., Gash, J.H., Holmes, T.R.H., de Jeu, R.A.M., Dolman, A.J., 2010. Global canopy interception from satellite observations. J. Geophys. Res.-Atmos. 115 (2009), 2009JD013530.

Miralles, D.G., Brutsaert, W., Dolman, A.J., Gash, J.H., 2020. On the use of the term “evapotranspiration”. Water Resour. Res. 56 (11) https://doi.org/10.1029/2020WR028055.

Y. Mitsuhata T. Ueda A. Kamimura S. Kato A. Takeuchi C. Aduma T. Yokota Development of a Drone-Borne Electromagnetic Survey System for Searching for Buried Vehicles and Soil Resistivity Mapping. 20 1 2022 16 29 10.1002/nsg.12189.

Morgan, S.E., Allen, D.M., Kirste, D., Salas, C.J., 2019. Investigating the hydraulic role of a large buried valley network on regional groundwater flow. Hydrgeol. J. 27 (7), 2377–2397. https://doi.org/10.1007/s10040-019-01995-0.

Morsy, M., Taghizadeh-Mehrjardi, R., Michaelides, S., Scholten, T., Dietrich, P., Schmidt, K., 2021. Optimization of rain gauge networks for arid regions based on remote sensing data. Remote Sens. (Basel) 13 (21), 4243.

Morway, E.D., Niswonger, R.G., Langevin, C.D., Bailey, R.T., Healy, R.W., 2012. Modeling variably saturated subsurface solute transport with MODFLOW-UFZ and MT3DMS. Ground Water 51 (2), no-no. https://doi.org/10.1111/j.1745-6584.2012.00971.x.

Mu, Q., Heinsch, F.A., Zhao, M., Running, S.W., 2007. Development of a global evapotranspiration algorithm based on MODIS and global meteorology data. Remote Sens. Environ. 106 (3), 285–304. https://doi.org/10.1016/j.rse.2006.07.007.

Mu, Q., Zhao, M., Running, S.W., 2011. Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sens. Environ. 115 (8), 1781–1800. https://doi.org/10.1016/j.rse.2011.02.019.

Mulder, V.L., de Bruin, S., Schaepman, M.E., Mayr, T.R., 2011. The use of remote sensing in soil and terrain mapping — a review. Geoderma 162 (1), 1–19. https://doi.org/10.1016/j.geoderma.2010.12.018.

Muller-Petke, M., Dlugosch, R., Yaramanci, U., 2011. Evaluation of surface nuclear magnetic resonance-estimated subsurface water content. New J. Phys. 13 https://doi.org/10.1088/1367-2630/13/9/095002.

Naz, B.S., Sharples, W., Ma, Y., Goergen, K., Kollet, S., 2023. Continental-scale evaluation of a fully distributed coupled land surface and groundwater model, ParFlow-CLM (v3.6.0), over Europe. Geosci. Model Dev. 16 (6), 1617–1639. https://doi.org/10.5194/gmd-16-1617-2023.

Niswonger, R.G., Prudic, D.E., Regan, R.S., 2006. Documentation of the Unsaturated-Zone Flow (UZFI) Package for modeling Unsaturated Flow Between the Land Surface and the Water Table with MODFLOW-2005. DOI:10.3133/TM6A19.

Niu, H.Y., Hollenbeck, D., Zhao, T.B., Wang, D., Chen, Y.Q., 2020. Evapotranspiration estimation with small UAVs in precision agriculture. Sensors 20 (22). https://doi.org/10.3390/s20226427.

Ntona, M.M., Busico, G., Mastrocicco, M., Kazakis, N., 2022. Modeling groundwater and surface water interaction: an overview of current status and future challenges. Sci. Total Environ. 846, 157355 https://doi.org/10.1016/j.scitotenv.2022.157355.

O'Connell, Y., Brown, C., Henry, T., Morrison, L., Daly, E., 2020. Quantitative assessment of groundwater resources using airborne electromagnetic remote sensing. J. Appl. Geophys. 175 https://doi.org/10.1016/j.jappgeo.2020.103990.

Olariu, H.G., Malambo, L., Popescu, S.C., Virgil, C., Wilcox, B.P., 2022. Woody plant encroachment: evaluating methodologies for semiarid Woody species classification from drone images. Remote Sens. (Basel) 14 (7), 1665.

O'Neill, M.M.F., Tijerina, D.T., Condon, L.E., Maxwell, R.M., 2021. Assessment of the ParFlow-CLM SUM 1.0 integrated hydrologic model: evaluation of hyper-resolution water balance components across the contiguous United States. Geosci. Model Dev. 14 (12), 7223–7254. https://doi.org/10.5194/gmd-14-7223-2021.

Pakoksung, K., Takagi, M., 2021. Assessment and comparison of digital elevation model (DEM) products in varying topographic, land cover regions and its attribute: a case study in Shikoku Island Japan. Modeling Earth Systems and Environment 7 (1), 465–484. https://doi.org/10.1007/s40808-020-00891-x.

Panday, S., Huyakorn, P.S., 2004. A fully coupled physically-based spatially-distributed model for evaluating surface/subsurface flow. Adv. Water Resour. 27 (4), 361–382. https://doi.org/10.1016/j.advwatres.2004.02.016.

Paniconi, C., Putti, M., 2015. Physically-based modeling in catchment hydrology at 50: survey and outlook. Water Resour. Res. 51 (9), 7090–7129.

Peng, J., Loew, A., Merlin, O., Verhoest, N.E.C., 2017. A review of spatial downscaling of satellite remotely sensed soil moisture. Rev. Geophys. 55 (2), 341–366. https://doi.org/10.1002/2016rg000543.

Pereira, L.S., Paredes, P., Hunsaker, D.J., Lopez-Urrea, R., Jovanovic, N., 2021a. Updates and advances to the FAO56 crop water requirements method. Agric. Water Manage. 248 https://doi.org/10.1016/j.agwat.2020.106697.

Pereira, L.S., Paredes, P., Hunsaker, D.J., López-Urrea, R., Mohammadi Shad, Z., 2021b. Standard single and basal crop coefficients for field crops, updates and advances to the FAO56 crop water requirements method. Agric. Water Manage. 243 https://doi.org/10.1016/j.AGWAT.2020.106466.

Pfeffer, J., Champollion, C., Favreau, G., Cappelaere, B., Hinderer, J., Boucher, M., Nazoumou, Y., Oi, M., Mouyen, M., Henri, C., Le Moigne, N., Deroussi, S., Demarty, J., Boulain, N., Benarrosh, N., Robert, O., 2013. Evaluating surface and subsurface water storage variations at small time and space scales from relative gravity measurements in semiarid Niger. Water Resour. Res. 49 (6), 3276–3291. https://doi.org/10.1002/wcr.20235.

Piccolroaz, S., Majone, B., Palmieri, F., Cassiani, G., Bellin, A., 2015. On the use of spatially distributed, time-lapse microgravity surveys to inform hydrological modeling. Water Resour. Res. 51 (9), 7270–7288. https://doi.org/10.1002/2015wr016994.

Poças, I., Calera, A., Campos, I., Cunha, M., 2020. Remote sensing for estimating and mapping single and basal crop coefficients: a review on spectral vegetation indices approaches. Agric. Water Manage. 233, 1–17. https://doi.org/10.1016/j.agwat.2020.106081.

Podgorski, J.E., Kinzelbach, W.K.H., Kgotlhang, L., 2017. Using helicopter TEM to delineate fresh water and salt water zones in the aquifer beneath the Okavango Delta, Botswana. Adv. Water Resour. 107, 265–279. https://doi.org/10.1016/j.advwatres.2017.06.021.

Pradhan, R.K., Markonis, Y., Godoy, M.R.V., Villalba-Pradas, A., Andreadis, K.M., Nikolopoulos, E.I., Papalexiou, S.M., Rahim, A., Tapiador, F.J., Hanel, M., 2022. Review of GPM IMERG performance: a global perspective. Remote Sens. Environ. 268 https://doi.org/10.1016/j.rse.2021.112754.

Qin, R., Liu, T., 2022. A review of landcover classification with very-high resolution remotely sensed optical images—Analysis unit. Model Scalability and Transferability. Remote Sensing 14 (3), 646.

Rafn, E.B., Contor, B., Ames, D.P., 2008. Evaluation of a method for estimating irrigated crop-evapotranspiration coefficients from remotely sensed data in Idaho. J. Irrig. Drain. Eng. 134 (6), 722–729. https://doi.org/10.1061/(asce)0733-9437(2008)134:6(722).

Rahmawati, N., Lubczynski, M.W., 2018. Validation of satellite daily rainfall estimates in complex terrain of Bali Island. Indonesia. Theoretical and Applied Climatology 134 (1–2), 513–532. https://doi.org/10.1007/s00704-017-2290-7.

Rateb, A., Scanlon, B.R., Pool, D.R., Sun, A., Zhang, Z.Z., Chen, J.L., Clark, B., Faunt, C. C., Haugh, C.J., Hill, M., Hobza, C., McGuire, V.L., Reitz, M., Schmied, H.M., Susanudjaja, E.H., Swenson, S., Wiese, D., Xia, Y.L., Zell, W., 2020. Comparison of groundwater storage changes from GRACE satellites with monitoring and modeling of major US aquifers. Water Resour. Res. 56 (12) https://doi.org/10.1029/2020wr027556.

Resop, J.P., Lehmann, L., Hession, W.C., 2021. Quantifying the spatial variability of annual and seasonal changes in riverscape vegetation using drone laser scanning. Drones 5 (3), 91.

Ridler, M.E., Madsen, H., Stisen, S., Bircher, S., Fensholt, R., 2014. Assimilation of SMOS-derived soil moisture in a fully integrated hydrological and soil-vegetation-atmosphere transfer model in Western Denmark. Water Resour. Res. 50 (11), 8962–8981. https://doi.org/10.1002/2014wr015392.

Rubin, Y., Hubbard, S.S., 2005. Hydrogeophysics. Springer, Dordrecht [Netherlands]; New York.

Sambuelli, L., Bava, S., 2012. Case study: a GPR survey on a morainic lake in northern Italy for bathymetry, water volume and sediment characterization. J. Appl. Geophys. 81, 48–56. https://doi.org/10.1016/j.jappgeo.2011.09.016.

Sass, G.Z., Creed, I.F., Riddell, J., Bayley, S.E., 2014. Regional-scale mapping of groundwater discharge zones using thermal satellite imagery. Hydrol. Process. 28 (23), 5662–5673. https://doi.org/10.1002/hyp.10068.

Savenije, H.H.G., 2004. The importance of interception and why we should delete the term evapotranspiration from our vocabulary. Hydrol. Process. 18 (8), 1507–1511. https://doi.org/10.1002/hyp.5563.

Scherer, D., Schwatke, C., Dettmering, D., Seitz, F., 2022. ICESat-2 based river surface slope and its impact on water level time series from satellite altimetry. Water Resour. Res. 58 (11), e2022WR032842 https://doi.org/10.1029/2022WR032842.

Schiilling, O.S., Cook, P.G., Brunner, P., 2019. Beyond classical methods in hydrogeology: the advantages of including exchange flux, temperature, tracer concentration, residence time, and soil moisture observations in groundwater model calibration. Rev. Geophys. 57 (1), 146–182. https://doi.org/10.1029/2018RG000619.

Schwingenschack, C., Hirschi, M., Seneviratne, S.I., 2017. Quantifying spatiotemporal variations of soil moisture control on surface energy balance and near-surface air temperature. J. Clim. 30 (18), 7105–7124. https://doi.org/10.1175/jcli-d-16-0727.1.

Seibert, J., Vis, M.J.P., 2016. How informative are stream level observations in different geographic regions? Hydrol. Process. 30 (14), 2498–2508. https://doi.org/10.1002/hyp.10887.

Senay, G.B., Friedrichs, M., Morton, C., Parrish, G.E.L., Schauer, M., Khand, K., Kagone, S., Boiko, O., Huntington, J., 2022. Mapping actual evapotranspiration using land grants for the conterminous United States: Google earth engine implementation and assessment of the SSEBop model. Remote Sens. Environ. 275 https://doi.org/10.1016/j.rse.2022.113011.

Seyoum, W.M., Milewski, A.M., 2017. Monitoring and comparison of terrestrial water storage changes in the northern high plains using GRACE and in-situ based integrated hydrologic model estimates. Adv. Water Resour. 94, 31–44. https://doi.org/10.1016/j.advwatres.2016.04.014.
and feedbacks. Water Resour. Res. 50 (2), 1531–1549. https://doi.org/10.1002/2013WR013725.

Maxwell, R.M., Condon, L.E., Kollet, S.J., 2015. A high-resolution simulation of groundwater surface water over most of the continental US with the integrated hydrologic model ParFlow v3. Geosci. Model Dev. 8 (3), 923–937. https://doi.org/10.5194/gmd-8-923-2015.

McJanlett, D., Wallace, J., Reddell, P., 2007. Precipitation interception in australian tropical rainforests: II. altitudinal gradients of cloud interception, stemflow, throughfall and interception. Hydrol. Process. 21 (13), 1703–1718. https://doi.org/10.1002/hyp.6346.

McMillan, W.D., Burgy, R.H., 1960. Interception loss from grass. Journal of Geophysical Research (1896-1977), 65(8): 2389-2394. DOI:https://doi.org/10.1029/JZ065i008p02389.

Milzow, C., Krogh, P.E., Bauer-Gottwein, P., 2011. Combining satellite radar altimetry, SAR surface soil moisture and GRACE total storage changes for hydrological model calibration in a large poorly gauged catchment. Hydrol. Earth Syst. Sci. 15 (6), 1729–1743. https://doi.org/10.5194/hess-15-1729-2011.

Miralles, D.G., Gash, J.H., Holmes, T.R.H., de Jeu, R.A.M., Dolman, A.J., 2010. Global canopy interception from satellite observations. J. Geophys. Res.-Atmos. 115 (2009), 2009JD013530.

Miralles, D.G., Brutsaert, W., Dolman, A.J., Gash, J.H., 2020. On the use of the term “evapotranspiration”. Water Resour. Res. 56 (11) https://doi.org/10.1029/2020wr028055.

Y. Mitsuhata T. Ueda A. Kamimura S. Kato A. Takeuchi C. Aduma T. Yokota Development of a Drone-Borne Electromagnetic Survey System for Searching for Buried Vehicles and Soil Resistivity Mapping. 20 1 2022 16 29 10.1002/nsg.12189.

Morgan, S.E., Allen, D.M., Kirste, D., Salas, C.J., 2019. Investigating the hydraulic role of a large buried valley network on regional groundwater flow. Hydrgeol. J. 27 (7), 2377–2397. https://doi.org/10.1007/s10040-019-01995-0.

Morsy, M., Taghizadeh-Mehrjardi, R., Michaelides, S., Scholten, T., Dietrich, P.,
Schmidt, K., 2021. Optimization of rain gauge networks for arid regions based on
remote sensing data. Remote Sens. (Basel) 13 (21), 4243.

Morway, E.D., Niswonger, R.G., Langevin, C.D., Bailey, R.T., Healy, R.W., 2012. Modeling variably saturated subsurface solute transport with MODFLOW-UFZ and MT3DMS. Ground Water 51 (2), no-no. https://doi.org/10.1111/j.1745-6584.2012.00971.x.

Mu, Q., Heinsch, F.A., Zhao, M., Running, S.W., 2007. Development of a global evapotranspiration algorithm based on MODIS and global meteorology data. Remote Sens. Environ. 106 (3), 285–304. https://doi.org/10.1016/j.rse.2006.07.007.

Mu, Q., Zhao, M., Running, S.W., 2011. Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sens. Environ. 115 (8), 1781–1800. https://doi.org/10.1016/j.rse.2011.02.019.

Mulder, V.L., de Bruin, S., Schaepman, M.E., Mayr, T.R., 2011. The use of remote sensing in soil and terrain mapping — a review. Geoderma 162 (1), 1–19. https://doi.org/10.1016/j.geoderma.2010.12.018.

Muller-Petke, M., Dlugosch, R., Yaramanci, U., 2011. Evaluation of surface nuclear magnetic resonance-estimated subsurface water content. New J. Phys. 13 https://doi.org/10.1088/1367-2630/13/9/095002.

Naz, B.S., Sharples, W., Ma, Y., Goergen, K., Kollet, S., 2023. Continental-scale evaluation of a fully distributed coupled land surface and groundwater model, ParFlow-CLM (v3.6.0), over Europe. Geosci. Model Dev. 16 (6), 1617–1639. https://doi.org/10.5194/gmd-16-1617-2023.

Niswonger, R.G., Prudic, D.E., Regan, R.S., 2006. Documentation of the Unsaturated-Zone Flow (UZFI) Package for modeling Unsaturated Flow Between the Land Surface and the Water Table with MODFLOW-2005. DOI:10.3133/TM6A19.

Niu, H.Y., Hollenbeck, D., Zhao, T.B., Wang, D., Chen, Y.Q., 2020. Evapotranspiration estimation with small UAVs in precision agriculture. Sensors 20 (22). https://doi.org/10.3390/s20226427.

Ntona, M.M., Busico, G., Mastrocicco, M., Kazakis, N., 2022. Modeling groundwater and surface water interaction: an overview of current status and future challenges. Sci. Total Environ. 846, 157355 https://doi.org/10.1016/j.scitotenv.2022.157355.

O'Connell, Y., Brown, C., Henry, T., Morrison, L., Daly, E., 2020. Quantitative assessment of groundwater resources using airborne electromagnetic remote sensing. J. Appl. Geophys. 175 https://doi.org/10.1016/j.jappgeo.2020.103990.

Olariu, H.G., Malambo, L., Popescu, S.C., Virgil, C., Wilcox, B.P., 2022. Woody plant encroachment: evaluating methodologies for semiarid Woody species classification from drone images. Remote Sens. (Basel) 14 (7), 1665.

O'Neill, M.M.F., Tijerina, D.T., Condon, L.E., Maxwell, R.M., 2021. Assessment of the ParFlow-CLM SUM 1.0 integrated hydrologic model: evaluation of hyper-resolution water balance components across the contiguous United States. Geosci. Model Dev. 14 (12), 7223-7254. https://doi.org/10.5194/gmd-14-7223-2021.

Pakoksung, K., Takagi, M., 2021. Assessment and comparison of digital elevation model (DEM) products in varying topographic, land cover regions and its attribute: a case study in Shikoku Island Japan. Modeling Earth Systems and Environment 7 (1), 465–484. https://doi.org/10.1007/s40808-020-00891-x.

Panday, S., Huyakorn, P.S., 2004. A fully coupled physically-based spatially-distributed model for evaluating surface/subsurface flow. Adv. Water Resour. 27 (4), 361–382. https://doi.org/10.1016/j.advwatres.2004.02.016.

Paniconi, C., Putti, M., 2015. Physically-based modeling in catchment hydrology at 50: survey and outlook. Water Resour. Res. 51 (9), 7090–7129.

Peng, J., Loew, A., Merlin, O., Verhoest, N.E.C., 2017. A review of spatial downscaling of satellite remotely sensed soil moisture. Rev. Geophys. 55 (2), 341–366. https://doi.org/10.1002/2016rg000543.

Pereira, L.S., Paredes, P., Hunsaker, D.J., Lopez-Urrea, R., Jovanovic, N., 2021a. Updates and advances to the FAO56 crop water requirements method. Agric. Water Manage. 248 https://doi.org/10.1016/j.agwat.2020.106697.

Pereira, L.S., Paredes, P., Hunsaker, D.J., López-Urrea, R., Mohammadi Shad, Z., 2021b. Standard single and basal crop coefficients for field crops, updates and advances to the FAO56 crop water requirements method. Agric. Water Manage. 243 https://doi.org/10.1016/j.AGWAT.2020.106466.

Pfeffer, J., Champollion, C., Favreau, G., Cappelaere, B., Hinderer, J., Boucher, M., Nazoumou, Y., Oi, M., Mouyen, M., Henri, C., Le Moigne, N., Deroussi, S., Demarty, J., Boulain, N., Benarrosh, N., Robert, O., 2013. Evaluating surface and subsurface water storage variations at small time and space scales from relative gravity measurements in semiarid Niger. Water Resour. Res. 49 (6), 3276–3291. https://doi.org/10.1002/wcr.20235.

Piccolroaz, S., Majone, B., Palmieri, F., Cassiani, G., Bellin, A., 2015. On the use of spatially distributed, time-lapse microgravity surveys to inform hydrological modeling. Water Resour. Res. 51 (9), 7270–7288. https://doi.org/10.1002/2015wr016994.

Poças, I., Calera, A., Campos, I., Cunha, M., 2020. Remote sensing for estimating and mapping single and basal crop coefficients: a review on spectral vegetation indices approaches. Agric. Water Manage. 233, 1–17. https://doi.org/10.1016/j.agwat.2020.106081.

Podgorski, J.E., Kinzelbach, W.K.H., Kgotlhang, L., 2017. Using helicopter TEM to delineate fresh water and salt water zones in the aquifer beneath the Okavango Delta, Botswana. Adv. Water Resour. 107, 265–279. https://doi.org/10.1016/j.advwatres.2017.06.021.

Pradhan, R.K., Markonis, Y., Godoy, M.R.V., Villalba-Pradas, A., Andreadis, K.M., Nikolopoulos, E.I., Papalexiou, S.M., Rahim, A., Tapiador, F.J., Hanel, M., 2022. Review of GPM IMERG performance: a global perspective. Remote Sens. Environ. 268 https://doi.org/10.1016/j.rse.2021.112754.

Qin, R., Liu, T., 2022. A review of landcover classification with very-high resolution remotely sensed optical images—Analysis unit. Model Scalability and Transferability. Remote Sensing 14 (3), 646.

Rafn, E.B., Contor, B., Ames, D.P., 2008. Evaluation of a method for estimating irrigated crop-evapotranspiration coefficients from remotely sensed data in Idaho. J. Irrig. Drain. Eng. 134 (6), 722–729. https://doi.org/10.1061/(asce)0733-9437(2008)134:6(722).

Rahmawati, N., Lubczynski, M.W., 2018. Validation of satellite daily rainfall estimates in complex terrain of Bali Island. Indonesia. Theoretical and Applied Climatology 134 (1–2), 513–532. https://doi.org/10.1007/s00704-017-2290-7.

Rateb, A., Scanlon, B.R., Pool, D.R., Sun, A., Zhang, Z.Z., Chen, J.L., Clark, B., Faunt, C. C., Haugh, C.J., Hill, M., Hobza, C., McGuire, V.L., Reitz, M., Schmied, H.M., Susanudjaja, E.H., Swenson, S., Wiese, D., Xia, Y.L., Zell, W., 2020. Comparison of groundwater storage changes from GRACE satellites with monitoring and modeling of major US aquifers. Water Resour. Res. 56 (12) https://doi.org/10.1029/2020wr027556.

Resop, J.P., Lehmann, L., Hession, W.C., 2021. Quantifying the spatial variability of annual and seasonal changes in riverscape vegetation using drone laser scanning. Drones 5 (3), 91.

Ridler, M.E., Madsen, H., Stisen, S., Bircher, S., Fensholt, R., 2014. Assimilation of SMOS-derived soil moisture in a fully integrated hydrological and soil-vegetation-atmosphere transfer model in Western Denmark. Water Resour. Res. 50 (11), 8962–8981. https://doi.org/10.1002/2014wr015392.

Rubin, Y., Hubbard, S.S., 2005. Hydrogeophysics. Springer, Dordrecht [Netherlands]; New York.

Sambuelli, L., Bava, S., 2012. Case study: a GPR survey on a morainic lake in northern Italy for bathymetry, water volume and sediment characterization. J. Appl. Geophys. 81, 48–56. https://doi.org/10.1016/j.jappgeo.2011.09.016.

Sass, G.Z., Creed, I.F., Riddell, J., Bayley, S.E., 2014. Regional-scale mapping of groundwater discharge zones using thermal satellite imagery. Hydrol. Process. 28 (23), 5662–5673. https://doi.org/10.1002/hyp.10068.

Savenije, H.H.G., 2004. The importance of interception and why we should delete the term evapotranspiration from our vocabulary. Hydrol. Process. 18 (8), 1507–1511. https://doi.org/10.1002/hyp.5563.

Scherer, D., Schwatke, C., Dettmering, D., Seitz, F., 2022. ICESat-2 based river surface slope and its impact on water level time series from satellite altimetry. Water Resour. Res. 58 (11), e2022WR032842 https://doi.org/10.1029/2022WR032842.

Schiilling, O.S., Cook, P.G., Brunner, P., 2019. Beyond classical methods in hydrogeology: the advantages of including exchange flux, temperature, tracer concentration, residence time, and soil moisture observations in groundwater model calibration. Rev. Geophys. 57 (1), 146–182. https://doi.org/10.1029/2018RG000619.

Schwingenschackl, C., Hirschi, M., Seneviratne, S.I., 2017. Quantifying spatiotemporal variations of soil moisture control on surface energy balance and near-surface air temperature. J. Clim. 30 (18), 7105–7124. https://doi.org/10.1175/jcli-d-16-0727.1.

Seibert, J., Vis, M.J.P., 2016. How informative are stream level observations in different geographic regions? Hydrol. Process. 30 (14), 2498–2508. https://doi.org/10.1002/hyp.10887.

Senay, G.B., Friedrichs, M., Morton, C., Parrish, G.E.L., Schauer, M., Khand, K., Kagone, S., Boiko, O., Huntington, J., 2022. Mapping actual evapotranspiration using landsat for the conterminous United States: Google earth engine implementation and assessment of the SSEBop model. Remote Sens. Environ. 275 https://doi.org/10.1016/j.rse.2022.113011.

Seyoum, W.M., Milewski, A.M., 2017. Monitoring and comparison of terrestrial water storage changes in the northern high plains using GRACE and in-situ based integrated hydrologic model estimates. Adv. Water Resour. 94, 31–44. https://doi.org/10.1016/j.advwatres.2016.04.014.
Shen, H., Leblanc, M., Tweed, S.O., Liu, W., 2015. Groundwater depletion in the Hai River basin, China, from in situ and GRACE observations. Hydrol. Sci. J. 60, 671–687.

Siemon, B., 2009. Electromagnetic methods – frequency domain. In: Kirsch, R. (Ed.), Groundwater Geophysics: A Tool for Hydrogeology. Springer, Berlin Heidelberg, Berlin, Heidelberg, pp. 155–178. https://doi.org/10.1007/978-3-540-88405-7_5.

Skibbe, N., Rochlitz, R., Gunther, T., Muller-Petke, M., 2020. Coupled magnetic resonance and electrical resistivity tomography: an open-source toolbox for surface nuclear-magnetic resonance. Geophysics 85 (3), F52–F63. https://doi.org/10.1190/geo2019-0484.1.

Steuer, A., Siemon, B., Auen, E., 2009. A comparison of helicopter-borne electromagnetics in frequency- and time-domain at the Cuxhaven valley in northern Germany. J. Appl. Geophys. 67 (3), 194–205. https://doi.org/10.1016/j.jappgeo.2007.07.001.

Sun, W.C., Fan, J., Wang, G.Q., Ishidaira, H., Bastola, S., Yu, J.S., Fu, Y.S.H., Kiem, A.S., Zuo, D.P., Xu, Z.X., 2018b. Calibrating a hydrological model in a regional river of the Qinghai-Tibet plateau using river water width determined from high spatial resolution satellite images. Remote Sens. Environ. 214, 100–114. https://doi.org/10.1016/j.rse.2018.05.020.

Sun, A.Y., Green, R., Swenson, S., Rodell, M., 2012. Toward calibration of regional groundwater models using GRACE data. J. Hydrol. 422, 1–9. https://doi.org/10.1016/j.jhydrol.2011.10.025.

Sun, Q.H., Miao, C.Y., Duan, Q.Y., Ashouri, H., Soroo shian, S., Hsu, K.L., 2018a. A review of global precipitation data sets: data sources, estimation, and intercomparisons. Rev. Geophys. 56 (1), 79–107. https://doi.org/10.1002/2017rg000574.

Szafarczyk, A., Toś, C., 2023. The use of green laser in LiDAR bathymetry: state of the art and recent advancements. Sensors 23 (1), 292.

Tamiminia, H., Salehi, B., Mahdianpari, M., Quackenbush, L., Adeli, S., Brisco, B., 2020. Google earth engine for geo-big data applications: a meta-analysis and systematic review. ISPRS J. Photogramm. Remote Sens. 164, 152–170. https://doi.org/10.1016/j.isprsjprs.2020.04.001.

Tang, G.Q., Clark, M.P., Papalexiou, S.M., Ma, Z.Q., Hong, Y., 2020. Have satellite precipitation products improved over last two decades? a comprehensive comparison of GPM IMERG with nine satellite and reanalysis datasets. Remote Sens. Environ. 240 https://doi.org/10.1016/j.rse.2020.111697.

Therrien, R., Sudicky, E.A., 1996. Three-dimensional analysis of variably-saturated flow and solute transport in discretely-fractured porous media. J. Contam. Hydrol. 23 (1), 1–44. https://doi.org/10.1016/0169-7722(95)00088-7.

Tian, Y., Peters-Lidard, C.D., Eylander, J.B., Joyce, R.J., Huffman, G.J., Adler, R.F., Hsu, K.-L., Turk, F.J., Garcia, M., Zeng, J., 2009. Component analysis of errors in satellite-based precipitation estimates. J. Geophys. Res. Atmos. 114 (D24) https://doi.org/10.1029/2009JD011949.

Tran, A.P., Bogaert, P., Wiaux, F., Vanclooster, M., Lambot, S., 2015. High-resolution space-time quantification of soil moisture along the hillslope using joint analysis of ground penetrating radar and frequency domain reflectometry data. J. Hydrol. 523, 252–261. https://doi.org/10.1016/j.jhydrol.2015.01.065.

Trepekli, K., Balström, T., Friborg, T., Fog, B., Allotey, A.N., Kofie, R.Y., Møller-Jensen, L., 2022. UAV-borne, LiDAR-based elevation modelling: a method for improving local-scale urban flood risk assessment. Nat. Hazards 113 (1), 423–451. https://doi.org/10.1007/s11069-022-05308-9.

Tribaldos, V.R., Ajo-Franklin, J.B., 2021. Aquifer monitoring using ambient seismic noise recorded with distributed acoustic sensing (DAS) deployed on dark fiber. journal of geophysical research-solid. Earth 126 (4). https://doi.org/10.1029/2020JB021004.

Trigo, I.F., de Bruin, H., Beyrich, F., Bosveld, F.C., Gavilan, P., Groh, J., Lopez-Urra, R., 2018. Validation of reference evapotranspiration from meteoas second generation (MSG) observations. Agric. For. Meteorol. 259, 271–285. https://doi.org/10.1016/j.agrformet.2018.05.008.

Tsiko, C.T., Makurira, H., Gerrits, A.M.J., Savenje, H.H.G., 2012. Measuring forest floor and canopy interception in a savannah ecosystem. Physics and Chemistry of the Earth, Parts a/b 47–48, 122–127. https://doi.org/10.1016/j.pce.2011.06.009.

Tweed, S.O., Leblanc, M., Webb, J.A., Lubczynski, M.W., 2007. Remote sensing and GIS for mapping groundwater recharge and discharge areas in salinity prone catchments, southeastern Australia. Hydrgeol. J. 15 (1), 75–96. https://doi.org/10.1007/s10040-006-0129-x.

Valence, E., Baraer, M., Rosa, E., Barbecot, F., Monty, C., 2022. Introducing drone-based GPR in snow hydrology studies. The Cryosphere Discuss. 2022, 1–25. https://doi.org/10.5194/tc-2022-42.

Vegas Galdos, F., Álvarez, C., García, A., Revilla, J.A., 2012. Estimated distributed rainfall interception using a simple conceptual model and moderate resolution imaging spectroradiometer (MODIS). J. Hydrol. 468–469, 213–228. https://doi.org/10.1016/j.jhydrol.2012.08.043.

Velez-Nicolas, M., Garcia-Lopez, S., Barbero, L., Ruiz-Ortiz, V., Sanchez-Bellon, A., 2021. Applications of unmanned aerial systems (UASs) in hydrology: a review. Remote Sens. (Basel) 13 (7). https://doi.org/10.3390/rs13071359.

Vibhute, A.D., Kale, K.V., Dhumal, R.K., Mehrotra, S.C., 2015. Soil type classification and mapping using hyperspectral remote sensing data, 2015 International Conference on Man and Machine Interfacing (MAMI), pp. 1-4. DOI:10.1109/MAMI.2015.7456607.

Vilhelmsen, T.N., Behroozmand, A.A., Christensen, S., Nielsen, T.H., 2014. Joint inversion of aquifer test, MRS, and TEM data. Water Resour. Res. 50 (5), 3956–3975. https://doi.org/10.1002/2013WR014679.

Vishwakarma, B.D., Zhang, J.W., Sneeuw, N., 2021. Downscaling GRACE total water storage change using partial least squares regression. Sci. Data 8 (1). https://doi.org/10.1038/s41597-021-00862-6.

Vouillamoz, J.M., Descloitres, M., Bernard, J., Fourcassier, P., Romagny, L., 2002. Application of integrated magnetic resonance sounding and resistivity methods for borehole implementation. a case study in Cambodia. J. Appl. Geophys. 50 (1–2), 67–81. https://doi.org/10.1016/s0926-9851(02)00130-1.

Vouillamoz, J.M., Lawson, F.M.A., Yalo, N., Descloitres, M., 2014. The use of magnetic resonance sounding for quantifying specific yield and transmissivity in hard aquifers: the example of Benin. J. Appl. Geophys. 107, 16–24. https://doi.org/10.1016/j.jappgeo.2014.05.012.

Vouillamoz, J.M., Valois, R., Lun, S., Caron, D., Arnout, L., 2016. Can groundwater secure drinking-water supply and supplementary irrigation in new settlements of north-West Cambodia? Hydrgeol. J. 24 (1), 195–209. https://doi.org/10.1007/s10040-015-1322-6.

Vu, H.M., Shanafield, M., Nhat, T.T., Partington, D., Batelaan, O., 2020. Mapping catchment-scale unmonitored groundwater abstractions: approaches based on soft data. Journal of Hydrology-Regional Studies 30. https://doi.org/10.1016/j.ejrhd.2020.100695.

Wahab, I., Hall, O., Jirström, M., 2018. Remote sensing of yields: application of UAV imagery-derived NDVI for estimating maize vigor and yields in complex farming Systems in sub-Saharan Africa. Drones 2 (3), 28.

Wambura, F.J., Dietrich, O., Lischeid, G., 2018. Improving a distributed hydrological model using evapotranspiration-related boundary conditions as additional constraints in a data-scarce river basin. Hydrol. Process. 32 (6), 759–775. https://doi.org/10.1002/HYP.11453.

White, J.T., Hunt, R.J., Fienen, M.N., Doherty, J.E., 2020. Approaches to highly parameterized inversion: PEST++ Version 5, a software suite for parameter estimation, uncertainty analysis, management optimization and sensitivity analysis. DOI:10.3133/TM7C26.

White, J.T., Fienen, M.N., Doherty, J.E., 2016. A python framework for environmental model uncertainty analysis. Environ. Model. Softw. 85, 217–228. https://doi.org/10.1016/j.envsoft.2016.08.017.

Wood, E.F., Roundy, J.K., Troy, T.J., van Beek, L.P.H., Bierkens, M.F.P., Blyth, E., de Roo, A., Döll, P., Ek, M., Famiglietti, J., Gochis, D., van de Giesen, N., Houser, P., Jaffé, P.R., Kollet, S., Lehner, B., Lettenmaier, D.P., Peters-Lidard, C., Sivapalan, M., Sheffield, J., Wade, A., Whitehead, P., 2011. Hyperresolution global land surface modeling: Meeting a grand challenge for monitoring Earth's terrestrial water. Water Res. 47 (5) https://doi.org/10.1029/2010WR010090.

Wu, K., Rodriguez, G.A., Zajc, M., Jacquemin, E., Clément, M., De Coster, A., Lambot, S., 2019. A new drone-borne GPR for soil moisture mapping. Remote Sens. Environ. 235, 111456 https://doi.org/10.1016/j.rse.2019.111456.

Wu, K., Desesquelles, H., Cockenpot, R., Guyard, L., Cuisiniez, V., Lambot, S., 2022. Ground-penetrating radar full-wave inversion for soil moisture mapping in Trench-Hill potato fields for precise irrigation. Remote Sens. (Basel) 14 (23), 6046.

Xu, X., Li, J., Tolson, B.A., 2014. Progress in integrating remote sensing data and hydrologic modeling. Progress Phys. Geograp.: Earth Environ. 38 (4), 464–498. https://doi.org/10.1177/0309133314536583.

Xu, S., 2021. Integrated Hydrological Modelling of Surface-groundwater Interactions in Hard Rock System of the Sardon Catchment (Spain) and Comparison with Selected Satellite Product. Msc Thesis of University of Twente (essay.utwente.nl/89002/).

Yang, X., Pavelsky, T.M., Allen, G.H., Donchys, G., 2020. RivWidthCloud: an automated Google earth engine algorithm for river width extraction from remotely sensed imagery. IEEE Geosci. Remote Sens. Lett. 17 (2), 217–221. https://doi.org/10.1109/lgrs.2019.2902205.

Yang, X., Hu, J., Ma, R., Sun, Z., 2021. Integrated hydrologic modelling of groundwater-surface water interactions in cold regions. Front. Earth Sci. 9 https://doi.org/10.3389/feart.2021.721009.

Zhang, K., Kimball, J.S., Running, S.W., 2016. A review of remote sensing based actual evapotranspiration estimation. Wiley Interdiscip. Rev.-Water 3 (6), 834–853. https://doi.org/10.1002/wat2.1168.

Zhang, H., Kurtz, W., Kollet, S., Vereecken, H., Franssen, H.J.H., 2018. Comparison of different assimilation methodologies of groundwater levels to improve predictions of root zone soil moisture with an integrated terrestrial system model. Adv. Water Resour. 111, 224–238. https://doi.org/10.1016/j.adwatres.2017.11.003.

Zhao, H.J., Montzka, C., Baatz, R., Vereecken, H., Franssen, H.J.H., 2021. The importance of subsurface processes in land surface modeling over a temperate region: an analysis with SMAP, cosmic ray neutron sensing and triple collocation analysis. Remote Sens. (Basel) 13 (16). https://doi.org/10.3390/rs13163068.

Zheng, G., Moskal, L.M., 2009. Retrieving leaf area index (LAI) using remote sensing: theories. Methods Sensors. 9 (4), 2719–2745.
Shen, H., Leblanc, M., Tweed, S.O., Liu, W., 2015. Groundwater depletion in the Hai River basin, China, from in situ and GRACE observations. Hydrol. Sci. J. 60, 671–687.

Siemon, B., 2009. Electromagnetic methods – frequency domain. In: Kirsch, R. (Ed.), Groundwater Geophysics: A Tool for Hydrogeology. Springer, Berlin Heidelberg, Berlin, Heidelberg, pp. 155–178. https://doi.org/10.1007/978-3-540-88405-7_5.

Skibbe, N., Rochlitz, R., Gunther, T., Muller-Petke, M., 2020. Coupled magnetic resonance and electrical resistivity tomography: an open-source toolbox for surface nuclear-magnetic resonance. Geophysics 85 (3), F52–F63. https://doi.org/10.1190/geo2019-0484.1.

Steuer, A., Siemon, B., Auen, E., 2009. A comparison of helicopter-borne electromagnetics in frequency- and time-domain at the Cuxhaven valley in northern Germany. J. Appl. Geophys. 67 (3), 194–205. https://doi.org/10.1016/j.jappgeo.2007.07.001.

Sun, W.C., Fan, J., Wang, G.Q., Ishidaira, H., Bastola, S., Yu, J.S., Fu, Y.S.H., Kiem, A.S., Zuo, D.P., Xu, Z.X., 2018b. Calibrating a hydrological model in a regional river of the Qinghai-Tibet plateau using river water width determined from high spatial resolution satellite images. Remote Sens. Environ. 214, 100–114. https://doi.org/10.1016/j.rse.2018.05.020.

Sun, A.Y., Green, R., Swenson, S., Rodell, M., 2012. Toward calibration of regional groundwater models using GRACE data. J. Hydrol. 422, 1–9. https://doi.org/10.1016/j.jhydrol.2011.10.025.

Sun, Q.H., Miao, C.Y., Duan, Q.Y., Ashouri, H., Sorooeshian, S., Hsu, K.L., 2018a. A review of global precipitation data sets: data sources, estimation, and intercomparisons. Rev. Geophys. 56 (1), 79–107. https://doi.org/10.1002/2017rg000574.

Szafarczyk, A., Toš, C., 2023. The use of green laser in LiDAR bathymetry: state of the art and recent advancements. Sensors 23 (1), 292.

Tamiminia, H., Salehi, B., Mahdianpari, M., Quackenbush, L., Adeli, S., Brisco, B., 2020. Google earth engine for geo-big data applications: a meta-analysis and systematic review. ISPRS J. Photogramm. Remote Sens. 164, 152–170. https://doi.org/10.1016/j.isprsjprs.2020.04.001.

Tang, G.Q., Clark, M.P., Papalexiou, S.M., Ma, Z.Q., Hong, Y., 2020. Have satellite precipitation products improved over last two decades? a comprehensive comparison of GPM IMERG with nine satellite and reanalysis datasets. Remote Sens. Environ. 240 https://doi.org/10.1016/j.rse.2020.111697.

Therrien, R., Sudicky, E.A., 1996. Three-dimensional analysis of variably-saturated flow and solute transport in discretely-fractured porous media. J. Contam. Hydrol. 23 (1), 1–44. https://doi.org/10.1016/0169-7722(95)00088-7.

Tian, Y., Peters-Lidard, C.D., Eylander, J.B., Joyce, R.J., Huffman, G.J., Adler, R.F., Hsu, K.-L., Turk, F.J., Garcia, M., Zeng, J., 2009. Component analysis of errors in satellite-based precipitation estimates. J. Geophys. Res. Atmos. 114 (D24) https://doi.org/10.1029/2009JD011949.

Tran, A.P., Bogaert, P., Wiaux, F., Vanclooster, M., Lambot, S., 2015. High-resolution space-time quantification of soil moisture along the hillslope using joint analysis of ground penetrating radar and frequency domain reflectometry data. J. Hydrol. 523, 252–261. https://doi.org/10.1016/j.jhydrol.2015.01.065.

Trepekli, K., Balström, T., Friborg, T., Fog, B., Allotey, A.N., Kofie, R.Y., Møller-Jensen, L., 2022. UAV-borne, LiDAR-based elevation modelling: a method for improving local-scale urban flood risk assessment. Nat. Hazards 113 (1), 423–451. https://doi.org/10.1007/s11069-022-05308-9.

Tribaldos, V.R., Ajo-Franklin, J.B., 2021. Aquifer monitoring using ambient seismic noise recorded with distributed acoustic sensing (DAS) deployed on dark fiber. journal of geophysical research-solid. Earth 126 (4). https://doi.org/10.1029/2020JB021004.

Trigo, I.F., de Bruin, H., Beyrich, F., Bosveld, F.C., Gavilan, P., Groh, J., Lopez-Urra, R., 2018. Validation of reference evapotranspiration from meteoat second generation (MSG) observations. Agric. For. Meteorol. 259, 271–285. https://doi.org/10.1016/j.agrformet.2018.05.008.

Tsiko, C.T., Makurira, H., Gerrits, A.M.J., Savenje, H.H.G., 2012. Measuring forest floor and canopy interception in a savannah ecosystem. Physics and Chemistry of the Earth, Parts a/b 47–48, 122–127. https://doi.org/10.1016/j.pce.2011.06.009.

Tweed, S.O., Leblanc, M., Webb, J.A., Lubczynski, M.W., 2007. Remote sensing and GIS for mapping groundwater recharge and discharge areas in salinity prone catchments, southeastern Australia. Hydrgeol. J. 15 (1), 75–96. https://doi.org/10.1007/s10040-006-0129-x.

Valence, E., Baraer, M., Rosa, E., Barbecot, F., Monty, C., 2022. Introducing drone-based GPR in snow hydrology studies. The Cryosphere Discuss. 2022, 1–25. https://doi.org/10.5194/tc-2022-42.

Vegas Galdos, F., Álvarez, C., García, A., Revilla, J.A., 2012. Estimated distributed rainfall interception using a simple conceptual model and moderate resolution imaging spectroradiometer (MODIS). J. Hydrol. 468–469, 213–228. https://doi.org/10.1016/j.jhydrol.2012.08.043.

Velez-Nicolas, M., Garcia-Lopez, S., Barbero, L., Ruiz-Ortiz, V., Sanchez-Bellon, A., 2021. Applications of unmanned aerial systems (UASs) in hydrology: a review. Remote Sens. (Basel) 13 (7). https://doi.org/10.3390/rs13071359.

Vibhute, A.D., Kale, K.V., Dhumal, R.K., Mehrotra, S.C., 2015. Soil type classification and mapping using hyperspectral remote sensing data, 2015 International Conference on Man and Machine Interfacing (MAMI), pp. 1-4. DOI:10.1109/MAMI.2015.7456607.

Vilhelmsen, T.N., Behroozmand, A.A., Christensen, S., Nielsen, T.H., 2014. Joint inversion of aquifer test, MRS, and TEM data. Water Resour. Res. 50 (5), 3956–3975. https://doi.org/10.1002/2013WR014679.

Vishwakarma, B.D., Zhang, J.W., Sneeuw, N., 2021. Downscaling GRACE total water storage change using partial least squares regression. Sci. Data 8 (1). https://doi.org/10.1038/s41597-021-00862-6.

Vouillamoz, J.M., Descloitres, M., Bernard, J., Fourcassier, P., Romagny, L., 2002. Application of integrated magnetic resonance sounding and resistivity methods for borehole implementation. a case study in Cambodia. J. Appl. Geophys. 50 (1-2), 67–81. https://doi.org/10.1016/s0926-9851(02)00130-1.

Vouillamoz, J.M., Lawson, F.M.A., Yalo, N., Descloitres, M., 2014. The use of magnetic resonance sounding for quantifying specific yield and transmissivity in hard aquifers: the example of Benin. J. Appl. Geophys. 107, 16–24. https://doi.org/10.1016/j.jappgeo.2014.05.012.

Vouillamoz, J.M., Valois, R., Lun, S., Caron, D., Arnout, L., 2016. Can groundwater secure drinking-water supply and supplementary irrigation in new settlements of north-West Cambodia? Hydrgeol. J. 24 (1), 195–209. https://doi.org/10.1007/s10040-015-1322-6.

Vu, H.M., Shanafield, M., Nhat, T.T., Partington, D., Batelaan, O., 2020. Mapping catchment-scale unmonitored groundwater abstractions: approaches based on soft data. Journal of Hydrology-Regional Studies 30. https://doi.org/10.1016/j.ejrh.2020.100695.

Wahab, I., Hall, O., Jirstöm, M., 2018. Remote sensing of yields: application of UAV imagery-derived NDVI for estimating maize vigor and yields in complex farming Systems in sub-Saharan Africa. Drones 2 (3), 28.

Wambura, F.J., Dietrich, O., Lischeid, G., 2018. Improving a distributed hydrological model using evapotranspiration-related boundary conditions as additional constraints in a data-scarce river basin. Hydrol. Process. 32 (6), 759–775. https://doi.org/10.1002/HYP.11453.

White, J.T., Hunt, R.J., Fienen, M.N., Doherty, J.E., 2020. Approaches to highly parameterized inversion: PEST++ Version 5, a software suite for parameter estimation, uncertainty analysis, management optimization and sensitivity analysis. DOI:10.3133/TM7C26.

White, J.T., Fienen, M.N., Doherty, J.E., 2016. A python framework for environmental model uncertainty analysis. Environ. Model. Softw. 85, 217–228. https://doi.org/10.1016/j.envsoft.2016.08.017.

Wood, E.F., Roundy, J.K., Troy, T.J., van Beek, L.P.H., Bierkens, M.F.P., Blyth, E., de Roo, A., Döll, P., Ek, M., Famiglietti, J., Gochis, D., van de Giesen, N., Houser, P., Jaffé, P.R., Kollet, S., Lehner, B., Lettenmaier, D.P., Peters-Lidard, C., Sivapalan, M., Sheffield, J., Wade, A., Whitehead, P., 2011. Hyperresolution global land surface modeling: Meeting a grand challenge for monitoring Earth's terrestrial water. Water Res. 47 (5) https://doi.org/10.1029/2010WR010090.

Wu, K., Rodriguez, G.A., Zajc, M., Jacquemin, E., Clément, M., De Coster, A., Lambot, S., 2019. A new drone-borne GPR for soil moisture mapping. Remote Sens. Environ. 235, 111456 https://doi.org/10.1016/j.rse.2019.111456.

Wu, K., Desesquelles, H., Cockenpot, R., Guyard, L., Cuisiniez, V., Lambot, S., 2022. Ground-penetrating radar full-wave inversion for soil moisture mapping in Trench-Hill potato fields for precise irrigation. Remote Sens. (Basel) 14 (23), 6046.

Xu, X., Li, J., Tolson, B.A., 2014. Progress in integrating remote sensing data and hydrologic modeling. Progress Phys. Geograp.: Earth Environ. 38 (4), 464–498. https://doi.org/10.1177/0309133314536583.

Xu, S., 2021. Integrated Hydrological Modelling of Surface-groundwater Interactions in Hard Rock System of the Sardon Catchment (Spain) and Comparison with Selected Satellite Product. Msc Thesis of University of Twente (essay.utwente.nl/89002/).

Yang, X., Pavelsky, T.M., Allen, G.H., Donchys, G., 2020. RivWidthCloud: an automated Google earth engine algorithm for river width extraction from remotely sensed imagery. IEEE Geosci. Remote Sens. Lett. 17 (2), 217–221. https://doi.org/10.1109/lgrs.2019.2902205.

Yang, X., Hu, J., Ma, R., Sun, Z., 2021. Integrated hydrologic modelling of groundwater-surface water interactions in cold regions. Front. Earth Sci. 9 https://doi.org/10.3389/feart.2021.721009.

Zhang, K., Kimball, J.S., Running, S.W., 2016. A review of remote sensing based actual evapotranspiration estimation. Wiley Interdiscip. Rev.-Water 3 (6), 834–853. https://doi.org/10.1002/wat2.1168.

Zhang, H., Kurtz, W., Kollet, S., Vereecken, H., Franssen, H.J.H., 2018. Comparison of different assimilation methodologies of groundwater levels to improve predictions of root zone soil moisture with an integrated terrestrial system model. Adv. Water Resour. 111, 224–238. https://doi.org/10.1016/j.adwatres.2017.11.003.

Zhao, H.J., Montzka, C., Baatz, R., Vereecken, H., Franssen, H.J.H., 2021. The importance of subsurface processes in land surface modeling over a temperate region: an analysis with SMAP, cosmic ray neutron sensing and triple collocation analysis. Remote Sens. (Basel) 13 (16). https://doi.org/10.3390/rs13163068.

Zheng, G., Moskal, L.M., 2009. Retrieving leaf area index (LAI) using remote sensing: theories. Methods Sensors. 9 (4), 2719–2745.