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Monitoring Earth's terrestrial water conditions is critically important to many hydrological applications such as global food production; assessing water resources sustainability; and flood, drought, and climate change prediction. These needs have motivated the development of pilot monitoring and prediction systems for terrestrial hydrologic and vegetative states, but to date only at the rather coarse spatial resolutions (∼10–100 km) over continental to global domains. Adequately addressing critical water cycle science questions and applications requires systems that are implemented globally at much higher resolutions, on the order of 1 km, resolutions referred to as hyperresolution in the context of global land surface models. This opinion paper sets forth the needs and benefits for a system that would monitor and predict the Earth's terrestrial water, energy, and biogeochemical cycles. We discuss six major challenges in developing a system: improved representation of surface-subsurface interactions due to fine-scale topography and vegetation; improved representation of land-atmospheric interactions and resulting spatial information on soil moisture and evapotranspiration; inclusion of water quality as part of the biogeochemical cycle; representation of human impacts from water management; utilizing massively parallel computer systems and recent computational advances in solving hyperresolution models that will have up to 109 unknowns; and developing the required in situ and remote sensing global data sets. We deem the development of a global hyperresolution model for monitoring the terrestrial water, energy, and biogeochemical cycles a “grand challenge” to the community, and we call upon the international hydrologic community and the hydrological science support infrastructure to endorse the effort.

The comparison of models in geosciences involves refining a single model or comparing various model structures. However, such model comparison studies are potentially invalid without considering the uncertainty estimates of observations in evaluating relative model performance. The temporal sampling of the observation and simulation time series is an additional source of uncertainty as a few observation and simulation pairs, in the form of outliers, might have a disproportionate effect on the model skill score. In this study we highlight the importance of including observation uncertainty and temporal sampling uncertainty when comparing or evaluating hydrological models. In hydrology, large-sample hydrology datasets contain a collection of catchments with hydro-meteorological time series, catchment boundaries and catchment attributes that provide an excellent test-bed for model evaluation and comparison studies. this study, two model experiments that cover different purposes for model evaluation are set up using 396 catchments from the CAMELS-GB dataset. The first experiment, intra-model, mimics a model refinement case by evaluating the streamflow estimates of the distributed wflow_sbm hydrological model with and without additional calibration. The second experiment, inter-model, is a model comparison based on the streamflow estimates of the distributed PCR-GLOBWB and wflow_sbm hydrological models. The temporal sampling uncertainty, the result of outliers in observation and simulation pairs, is found to be substantial throughout the case study area. High temporal sampling uncertainty indicates that the model skill scores used to evaluate model performance are heavily influenced by only a few data points in the time series. This is the case for half of the simulations 210) of the first intra-model experiment and 53 catchment simulations of the second inter-model experiment as indicated by larger sampling uncertainty than the difference in the KGE-NP model skill score. These cases highlight the importance of reporting and determining the cause of temporal sampling uncertainty before drawing conclusions on large-sample hydrology based model performance. The streamflow observation uncertainty analysis shows similar results. One third of the catchments simulations (123) of the intra-model experiment contains smaller streamflow simulation differences between models than streamflow observation uncertainties, compared to only 4 catchment simulations of the inter-model experiment due to larger differences between streamflow simulations. These catchments simulations should be excluded before drawing conclusions based on large-samples of catchments. The results of this study demonstrate that it is crucial for benchmark efforts based on large-samples of catchments to include streamflow observation uncertainty and temporal sampling uncertainty to obtain more robust results. [ABSTRACT FROM AUTHOR]

West African food systems and rural socio-economics are based on rainfed agriculture, which makes society highly vulnerable to rainfall uncertainty and frequent floods and droughts. Reliable rainfall information is currently missing. There is a sparse and uneven rain gauge distribution and, despite continuous efforts, rainfall satellite products continue to show weak correlations with ground measurements. This paper aims to investigate whether water vapor (WV) observations together with temporal information can complement thermal infrared (TIR) data for satellite rainfall retrieval in a Deep Learning (DL) framework. This is motivated by the fact that water vapor plays a key role in the highly seasonal West African rainfall dynamics. We present a DL model for satellite rainfall detection based on WV and TIR channels of Meteosat Second Generation and temporal information. Results show that the WV inhibition of low-level features enables the depiction of strong convective motions usually related to heavy rainfall. This is especially relevant in areas where convective rainfall is dominant, such as the tropics. Additionally, WV data allow us to detect dry air masses over our study area, that are advected from the Sahara Desert and create discontinuities in precipitation events. The developed DL model shows strong performance in rainfall binary classification, with less false alarms and lower rainfall overdetection (FBias < 2.0 ) than the state-of-the-art Integrated MultisatellitE Retrievals for GPM (IMERG) Final Run. [ABSTRACT FROM AUTHOR]

Complex African ecosystems such as the Miombo Woodland, with unique plant phenology, have evaporation dynamics that have not been investigated due to very few, if at all existant, flux tower observations. Furthermore, significant differences have been observed in satellite-based evaporation estimates in the Miombo Woodland especially in the dry season. Therefore, deciding which sattelite evaporation product to use in this ecosystem is difficult, as these products vary in many respects. In this study, the actual evaporation estimates for six satellite-based evaporation estimates are compared across Miombo Woodland phenophases in the Luangwa Basin, in southern Africa. In the absence of basin scale field observations, the actual evaporation estimated using the general water balance is used as reference, to which the six satellite-based evaporation estimates have been compared. Our results show significant variation in actual evaporation estimates in the water limited, high temperature and lower forest canopy cover and leaf chlorophyll conditions in the dormant phenophase. Lowest variation is observed in water abundant, high temperature, high leaf chlorophyll content and high forest canopy cover in the maturity/peak phenophase(s). Compared to the basin scale water balance actual evaporation, all six satellite-based evaporation estimates appear to underestimate evaporation. The results of underestimation at basin scale agrees with local field observations in a dense Miombo Woodland in the Luangwa Basin, which indicates that satellite-based evaporation estimates generally underestimate dry season (dormant phenophase) and early rain season (green-up phenophase) actual evaporation. The discrepancies in dry season satellite-based evaporation estimates may be cuased by the Miombo Woodland species' phenological adaptation attributes such as: leaf fall, leaf flush, access to deep soil moisture and the within vegetation water storage, coupled with heterogenous plant species response to phenological stimuli. Therefore, it appears that satellite-based evaporation estimates using model structure, processes and inputs that are capable of capturing Miombo species dry season phenological interaction with climate are likely to have actual evaporation estimates closer to field conditions. [ABSTRACT FROM AUTHOR]

Atmospheric water demand drives forest evaporation controlled by the plant physiological properties within available moisture storage thresholds. The pattern and magnitude of African Miombo Forest transpiration across dry season canopy phenophases are unknown. This is because estimating forest evaporation in African ecosystems continues to be a challenge as flux observation towers are scant, if not completely lacking in most ecosystems like the Miombo Forest, one of Africa’s largest woodland formations. Moreover, in the Miombo Forest, satellite data-based evaporation products (i.e., GLEAM, MOD16, SSEBop and WaPOR) show significant discrepancies in both pattern and amounts of evaporation especially during the dry season canopy phenophases. Despite the main limitations with estimation of forest evaporation the development and application of the distributed temperature sensing (DTS) system is providing deepened insights and improved accuracy in forest energy partitioning for evaporation assessment. In this study the Bowen ratio distributed temperature sensing (BR-DTS) approach is used to partition available energy and estimate evaporation across three Miombo Forest canopy phenophases covering the entire 2021 dry season and early rain season. Furthermore, four satellite evaporation products are compared to the field observations. Results show that evaporation appears to follow the net radiation and air temperature pattern with the lowest values observed during the most net radiation and air temperature depressed periods and highest values during the peak net radiation and air temperature. Evaporation continues to rise even during the driest period in the dormant leaf phenophase when canopy cover is said to be at its minimum. This is possibly facilitated by the retention of about 70 percent canopy cover during the dry season which transpires within the adapted thresholds constrained by physiological properties of each Miombo Forest species with access to ground water and vegetative water storage. This goes to show that during the dry season Miombo species may not be as water stressed as imagined. When compared to field observations all four-satellite evaporation products underestimate evaporation with only the WaPOR showing a similar pattern of evaporation during the dry season. The differences between field observations and satellite-based evaporation products can be attributed to the model structure, processes as well as inputs. [ABSTRACT FROM AUTHOR]

In semiarid regions, thousands of small reservoirs provide the rural population with water, but their storage volumes and hydrological impact are largely unknown. This paper analyzes the suitability of weather-independent radar satellite images for monitoring small reservoir surfaces. The surface areas of three reservoirs were extracted from 21 of 22 ENVISAT Advanced Synthetic Aperture Radar scenes, acquired bimonthly from June 2005 to August 2006. The reservoir surface areas were determined with a quasi-manual classification approach, as stringent classification rules often failed due to the spatial and temporal variability of the backscatter from the water. The land-water contrast is critical for the detection of water bodies. Additionally, wind has a significant impact on the classification results and affects the water surface and the backscattered radar signal (Bragg scattering) above a wind speed threshold of 2.6 m x [s.sup.-1]. The analysis of 15 months of wind speed data shows that, on 96% of the days, wind speeds were below the Bragg scattering criterion at the time of night time acquisitions, as opposed to 50% during the morning acquisition time. Night time acquisitions are strongly advisable over day time acquisitions due to lower wind interference. Over the year, radar images are most affected by wind during the onset of the rainy season (May and June). We conclude that radar and optical systems are complimentary. Radar is suitable during the rainy season but is affected by wind and lack of vegetation context during the dry season. Index Terms--Bragg scattering, radar, remote sensing, reservoirs, resource management, water, water resources, West Africa.

Accurate spatial-temporal information on evaporation is needed for use in many sectors including hydrology, agriculture and climate studies. This would require a dense observation network, which is practically impossible. Over the past decades, remotely sensed evaporation models to estimate spatially continuous evaporation have been developed. However, deciding which model to use is a challenge as these models vary in complexity and accuracy across the different global ecosystems. It is even more challenging for complex African ecosystems that have very few, or none at all, flux tower observations. In this study, we used the general water balance evaporation as reference to which we compared six models that determine evaporation, i.e., TerraClimate, GLEAM, MOD16, SSEBop and WaPOR, in the Luangwa Basin, a semi-arid catchment in the Miombo ecosystem in southern Africa. and models are calibrated on discharge, while GLEAM, MOD16, SSEBop and WaPOR have been validated on evaporation data from flux tower observations. Key focus is on inter-model performance comparison in the Miombo ecosystem across phenophases and land cover types. Results show that major spatial-temporal discrepancies in model performance occur in the forest and open water body land surfaces during the dormant and green-up phenophases in the dry season. Compared to, annually WaPOR consistently overestimated evaporation while GLEAM consistently underestimated evaporation. The rest of the models showed biases within the GLEAM and WaPOR boundaries. With reference to annual mean bias SSEBop and WaPOR showed lowest aggregated 2009 -2020 bias in terms of estimating long-term average annual evaporation. It appears that correct understanding of the Miombo vegetation phenology associated moisture feedbacks and incorporating these in model structure is likely to improve evaporation estimates in the Luangwa Basin and Miombo Woodland ecosystem as a whole. [ABSTRACT FROM AUTHOR]

Current global satellite scatterometer-based soil moisture retrieval algorithms do not take soil characteristics into account. In this paper, the characteristic time length of the soil water index has been calibrated for ten sampling frequencies and for different soil conductivity associated with 12 soil texture classes. The calibration experiment was independently performed from satellite observations. The reference soil moisture data set was created with a 1-D water-flow model and by making use of precipitation measurements. The soil water index was simulated by applying the algorithm to the modeled soil moisture of the upper few centimeters. The resulting optimized characteristic time lengths T increase with longer sampling periods. For instance, a T of 7 days was found for sandy soil when a sampling period of 1 day was applied, whereas an optimized T-value of 18 days was found for a sampling period of 10 days. A maximum rmse improvement of 0.5% vol. can be expected when using the calibrated T-values instead of T = 20. The soil water index and the differentiated T-values were applied to European Remote Sensing (ERS) satellite scatterometer data and were validated against in situ soil moisture measurements. The results obtained using calibrated T-values and T = 20 did not differ (r = 0.39, rmse = 5.4% vol.) and can be explained by the averaged sampling period of 4-5 days. The soil water index obtained with current operational microwave sensors [Advanced Wind Scatterometer (ASCAT) and Advanced Microwave Scanning Radiometer--Earth Observation System] and future sensors (Soil Moisture and Ocean Salinity and Soil Moisture Active Passive) should benefit from soil texture differentiation, as they can record on a daily basis either individually or synergistically using several sensors. The proposed differentiated characteristic time length enables the continuation of the soil water index of sensors with varying sampling periods (e.g., ERS-ASCAT). Index Terms--Hydrology, microwave, scatterometer, soil moisture, soil texture.

In West Africa, which is an extremely moisture-limited region, soil water information plays a vital role in hydrologic and meteorologic modeling for improved water resource planning and food security. Recent and upcoming satellite missions, such as SMOS and MetOp, hold promise for the regional observation of soil moisture. The resolution of the satellites is relatively coarse (> 100 [km.sup.2]), which brings with it the need for large-scale soil moisture information for calibration and validation purposes. We put forward a soil moisture sampling protocol based on hydrotopes. Hydrotopes are defined as landscape units that show internally consistent hydrologic behavior. This hydrotope analysis helps in the following ways: 1) by ensuring statistically reliable validation via the reduction of the overall pixel variance and 2) by improving sampling schemes for ground truthing by reducing the chance of sampling bias. As a sample application, we present data from three locations with different moisture regimes within the Volta Basin during both dry and wet periods. Results show that different levels of reduction in the overall pixel variance of soil moisture are obtained, depending on the general moisture status. With respect to the distinction between the different hydrotope units, it is shown that under intermediate moisture conditions, the distinction between the different hydrotope units is highest, whereas extremely dry or wet conditions tend to have a homogenizing effect on the spatial soil moisture distribution. This paper confirms that well-defined hydrotope units yield an improvement at pixel-scale soil moisture averages that can easily be applied. Index Terms--Geostatistics, hydrotopes, MetOP, satellites, scaling, soil moisture, Soil Moisture and Ocean Salinity (SMOS), West Africa.