Estimating the water balance and uncertainty bounds in a highly groundwater-dependent and data-scarce areas: An example for the upper Citarum basin

<p>In this study, we estimate the water balance components of a highly groundwater-dependent and hydrological data-scarce basin of the upper reaches of the Citarum river in West Java, Indonesia. Firstly, we estimate the groundwater abstraction volumes based on population size and a review of literature (0.57mm/day). Estimates of other components like rainfall, actual evaporation, discharge, and total water storage changes are derived from global datasets and are simulated using a distributed hydrological wflow_sbm model which yields additional estimates of discharge, actual evaporation, and total water storage change. We compare each basin water balance estimate as well as quantify the uncertainty of some of the components using the Extended Triple Collocation (ETC) method.</p><p>The ETC application on four different rainfall estimates suggests a preference of using the CHIRPS product as the input to the water balance components estimates as it delivers the highest r<sup>2</sup>  and the lowest RMSE compared to three other sources. From the different data sources and results of the distributed hydrological modeling using CHIRPS as rainfall forcing, we estimate a positive groundwater storage change between 0.12 mm/day - 0.60 mm/day. These results are in agreement with groundwater storage change estimates based upon GRACE gravimetric satellite data, averaged at 0.25 mm/day. The positive groundwater storage change suggests sufficient groundwater recharge occurs compensating for groundwater abstraction. This conclusion seems in agreement with the observation since 2005, although measured in different magnitudes. To validate and narrow the estimated ranges of the basin water storage changes, a devoted groundwater model is necessary to be developed. The result shall also aid in assessing the current and future basin-scale groundwater level changes to support operational water management and policy in the Upper Citarum basin.</p>

Actual evaporation from bare soils - A comparison of numerical modelling and field lysimeter data

<p>Predicting evaporation from drying soils under limited water supply conditions, where water transfer to the atmosphere is limited primarily by soil hydraulic conductivity, is challenging. The parameterization of soil hydraulic properties (SHP) plays a crucial role in reliable predictions of evaporation. In particular, there are expected differences between traditional functions that consider water flow only in capillaries and functions that additionally consider non-capillary processes, i.e., water storage and film flow on particle surfaces and in corners and channels of pores. The non-capillary processes in simulating evaporation from soil surfaces become more important when the soil dries.</p><p>The purpose of this study was to investigate the applicability of different soil hydraulic function types in modelling the actual evaporation under water-limited conditions. Data were obtained from a large bare-soil field lysimeter (2.5 m height; 1 m<sup>2</sup> surface area), where the lysimeter mass and outflow were measured in hourly time intervals. Precipitation and actual evaporation were calculated from the mass changes of the lysimeter, using a simplified version of the AWAT filter approach of Peters et al. (2017). Meteorological parameters to calculate the potential evaporation were taken from the nearest weather station. Potential evaporation rates were obtained by (i) using the FAO-56 version of the Penman-Monteith equation and (ii) scaling these values to match the bare soil potential evaporation.</p><p>The evaporation was simulated using two different models for soil hydraulic properties: i) van Genuchten Mualem (VGM) (only capillary storage and flow), and ii) Peters-Durner-Iden (PDI) (capillary and non-capillary storage and flow). The results show a systematic difference in evaporation prediction by applying the PDI and VGM models, with higher evaporation rates for the PDI model under dry conditions.</p>

Suitability of 17 gridded rainfall and temperature datasets for large-scale hydrological modelling in West Africa

This study evaluates the ability of different gridded rainfall datasets to plausibly represent the spatio-temporal patterns of multiple hydrological processes (i.e. streamflow, actual evaporation, soil moisture and terrestrial water storage) for large-scale hydrological modelling in the predominantly semi-arid Volta River basin (VRB) in West Africa. Seventeen precipitation products based essentially on gauge-corrected satellite data (TAMSAT, CHIRPS, ARC, RFE, MSWEP, GSMaP, PERSIANN-CDR, CMORPH-CRT, TRMM 3B42 and TRMM 3B42RT) and on reanalysis (ERA5, PGF, EWEMBI, WFDEI-GPCC, WFDEI-CRU, MERRA-2 and JRA-55) are compared as input for the fully distributed mesoscale Hydrologic Model (mHM). To assess the model sensitivity to meteorological forcing during rainfall partitioning into evaporation and runoff, six different temperature reanalysis datasets are used in combination with the precipitation datasets, which results in evaluating 102 combinations of rainfall–temperature input data. The model is recalibrated for each of the 102 input combinations, and the model responses are evaluated by using in situ streamflow data and satellite remote-sensing datasets from GLEAM evaporation, ESA CCI soil moisture and GRACE terrestrial water storage. A bias-insensitive metric is used to assess the impact of meteorological forcing on the simulation of the spatial patterns of hydrological processes. The results of the process-based evaluation show that the rainfall datasets have contrasting performances across the four climatic zones present in the VRB. The top three best-performing rainfall datasets are TAMSAT, CHIRPS and PERSIANN-CDR for streamflow; ARC, RFE and CMORPH-CRT for terrestrial water storage; MERRA-2, EWEMBI/WFDEI-GPCC and PGF for the temporal dynamics of soil moisture; MSWEP, TAMSAT and ARC for the spatial patterns of soil moisture; ARC, RFE and GSMaP-std for the temporal dynamics of actual evaporation; and MSWEP, TAMSAT and MERRA-2 for the spatial patterns of actual evaporation. No single rainfall or temperature dataset consistently ranks first in reproducing the spatio-temporal variability of all hydrological processes. A dataset that is best in reproducing the temporal dynamics is not necessarily the best for the spatial patterns. In addition, the results suggest that there is more uncertainty in representing the spatial patterns of hydrological processes than their temporal dynamics. Finally, some region-tailored datasets outperform the global datasets, thereby stressing the necessity and importance of regional evaluation studies for satellite and reanalysis meteorological datasets, which are increasingly becoming an alternative to in situ measurements in data-scarce regions.

A review of the complementary principle of evaporation: from the original linear relationship to generalized nonlinear functions

The complementary principle is an important methodology for estimating actual evaporation by using routinely observed meteorological variables. This review summaries its 56-year development, focusing on how related studies have shifted from adopting a symmetric linear complementary relationship (CR) to employing generalized nonlinear functions. The original CR denotes that the actual evaporation (E) and “apparent” potential evaporation (Epa) depart from the potential evaporation (Ep0) complementarily when the land surface dries from a completely wet environment with constant available energy. The CR was then extended to an asymmetric linear relationship, and the linear nature was retained through properly formulating Epa and/or Ep0. Recently, the linear CR was generalized to a sigmoid function and a polynomial function. The sigmoid function does not involve the formulations of Epa and Ep0 but uses the Penman (1948) potential evaporation and its radiation component as inputs, whereas the polynomial function inherits Ep0 and Epa as inputs and requires proper formulations for application. The generalized complementary principle has a more rigorous physical base and offers a great potential in advancing evaporation estimation. Future studies may cover several topics, including the boundary conditions in wet environments, the parameterization and application over different regions of the world, and integration with other approaches for further development.

Evaluation of precipitation and actual evaporation products over the Nile Basin

<p>An improved representation of the spatio-temporal patterns of climatological variables is crucial for ecological, agricultural, and hydrological applications and can improve the decision-making process. Traditionally, precipitation (P) and actual evaporation (ETa) are estimated using ground-based measurements from meteorological stations. However, the estimation of spatial patterns derived solely from point-based measurements is subject to large uncertainties, particularly in data-scarce regions as the Nile Basin, which has an area of about 3 million km<sup>2</sup>. This study evaluates six state-of-the-art P products (CHIRPSv2, CMORPHv1, CRU TS4.02, MSWEPv2.2, PERSIANN-CDR and GPCCv2018) and five ETa products (SSEBop, MOD16-ET, WaPOR, GLEAM and GLDAS) over the Nile Basin to identify the best-performing products. The P products were evaluated at monthly and annual temporal scales (from 1983 onwards) through a point-to-pixel approach using the modified Kling-Gupta Efficiency and its components (linear correlation, bias, and variability ratio) as continuous performance indices. The ETa products were evaluated through the water balance approach (due to the lack of ground-based ETa measurements) for 2009-2018 at the multiannual scale. Because streamflow data were not available for this period, an empirical model based on the Random Forest machine learning technique was used to estimate streamflow at 21 catchments at the monthly scale. For this purpose, we used streamflow data from 1983 to 2005 as the dependent variable, while CHIRPSv2 precipitation and ERA5 potential evaporation and temperature data were used as predictors. For the catchments where the model performed well over the validation period, streamflow estimates were generated and used for the water balance analysis. Our results show that CHIRPSv2 was the best performing P product at monthly and annual scale when compared with ground-based measurements, while WaPOR was the best-performing ETa product in the water balance evaluation. This study demonstrates how remote sensing data can be evaluated over extremely data-scarce scenarios to estimate the magnitude of key meteorological variables, yet also highlights the importance of improving data availability so that the characterisation of these variables can be further evaluated and improved.</p>

Spatial variability of mean daily estimates of actual evaporation from remotely sensed imagery and surface reference data

Land surface evaporation has considerable spatial variability that is not captured by point-scale estimates calculated from meteorological data alone. Knowing how evaporation varies spatially remains an important issue for improving parameterisations of land surface schemes and hydrological models and various land management practices. Satellite-based and aerial remote sensing has been crucial for capturing moderate- to larger-scale surface variables to indirectly estimate evaporative fluxes. However, more recent advances for field research via unmanned aerial vehicles (UAVs) now allow for the acquisition of more highly detailed surface data. Integrating models that can estimate “actual” evaporation from higher-resolution imagery and surface reference data would be valuable to better examine potential impacts of local variations in evaporation on upscaled estimates. This study introduces a novel approach for computing a normalised ratiometric index from surface variables that can be used to obtain more-realistic distributed estimates of actual evaporation. For demonstration purposes the Granger–Gray evaporation model (Granger and Gray, 1989) was applied at a rolling prairie agricultural site in central Saskatchewan, Canada. Visible and thermal images and meteorological reference data required to parameterise the model were obtained at midday. Ratiometric indexes were computed for the key surface variables albedo and net radiation at midday. This allowed point observations of albedo and mean daily net radiation to be scaled across high-resolution images over a large study region. Albedo and net radiation estimates were within 5 %–10 % of measured values. A daily evaporation estimate for a grassed surface was 0.5 mm (23 %) larger than eddy covariance measurements. Spatial variations in key factors driving evaporation and their impacts on upscaled evaporation estimates are also discussed. The methods applied have two key advantages for estimating evaporation over previous remote-sensing approaches: (1) detailed daily estimates of actual evaporation can be directly obtained using a physically based evaporation model, and (2) analysis of more-detailed and more-reliable evaporation estimates may lead to improved methods for upscaling evaporative fluxes to larger areas.

Spatial variability of mean daily estimates of actual evaporation from remotely sensed imagery and surface reference data

Land surface evaporation has considerable spatial variability that is not captured by point scale estimates calculated from meteorological data alone. Knowing how evaporation varies spatially remains an important issue for improving parameterisations of land surface schemes and hydrological models, and various land management practices. Satellite-based and aerial remote sensing has been crucial for capturing moderate to larger scale surface variables to indirectly estimate evaporative fluxes. However, more recent advances for field research via unmanned aerial vehicles (UAVs) now allows for the acquisition of more highly detailed surface data. Integrating models that can estimate actual evaporation from higher resolution imagery and surface reference data would be valuable to better examine potential impacts of local variations in evaporation on upscaled estimates. This study introduces a novel approach for computing a normalised index from surface variables that can be used to obtain more realistic distributed estimates of actual evaporation. For demonstration purposes the Granger and Gray evaporation model (G–D) was applied at a complex parkland site in central Saskatchewan, Canada. Visible and thermal images and meteorological reference data required to parameterise the model was obtained at midday. Normalised indexes (simple ratios) were computed at midday for albedo and net radiation. This allowed for single measured values albedo and mean daily net radiation to be scaled across high resolution images over a large study region. Albedo and net radiation estimates were within 5–10 % of measured values. An evaporation estimate for a grassed surface was 0.5 mm larger than eddy covariance measurements. The methods applied have two key advantages for estimating evaporation over previous remote sensing approaches, 1. Detailed daily estimates of actual evaporation were directly obtained using a physically-based evaporation model, and 2. Analysis of more detailed and reliable evaporation estimates may lead to improved methods for upscaling evaporative fluxes to larger scales.