Data-scarce Regions Research Articles

Intercomparison of gridded precipitation datasets for prospective hydrological applications in Sri Lanka

AbstractThe aim of this study was to assess the application of gridded precipitation datasets for applications in Sri Lanka and to identify suitable products that can be used for hydrological applications in data sparse regions. Here, nine global datasets were considered for this study, and after initial screening, four datasets, such as Integrated Multi‐Satellite Retrievals for GPM (IMERG) early run calibrated, Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), European Center for Medium range Weather Forecasting (ECMWF)—ReAnalysis (ERA5) and Indian Monsoon Data Assimilation and Reanalysis (IMDAA), were selected for detailed comparison. These data products were analysed at monthly, seasonal and annual time scale for whole Sri Lanka and also for three distinguished climate zones (wet, intermediate and dry) for the period 2001–2015. The datasets were further evaluated based on their ability to capture the extreme events, based on recent designated dry year and wet year. All four datasets represent the annual and monthly climatology of Sri Lanka well; however, discrepancies are seen at seasonal scale. CHIRPS had the best performance in the wet zone and ERA5 in dry and intermediate zones. Although all datasets distinctly identified the wet year and dry year with respect to the normal average, the interannual variability was explicitly identified by CHIRPS and ERA5. Therefore, for replicating the national and zonal climatology, both ERA5 (dry and intermediate zones) and CHIRPS (wet zone) are suitable. With the perspective of hydrological applications, both datasets can be used in development of hydrological models for applications such as flood forecasting, reservoir management and climate change impact assessment as an alternative to observed rainfall data in data scarce regions.

Evaluation of hydrological modeling using climatic station and gridded precipitation dataset

The conventional rainfall data estimates are relatively accurate at some points of the region. The interpolation of such type of data approximates the actual rainfield however in data scarce regions; the resulted rainfield is the rough estimate of the actual rainfall events. In data scarce regions like Indus basin Pakistan, the data obtained through remote sensing can be very useful. This research evaluates two types of gridded data i.e., European Reanalysis (ERA) interim and Japanese Reanalysis 55 years (JRA-55) along with the climatic station data for three small dams in Pakistan. Since no measured flow data is available at these dams, the nearest possible catchments where flow data is available are calibrated and the calibrated parameters of these catchments are then used in actual dams for simulating the flow from all the three types of data using Soil and Water Assessment Tool (SWAT). The results of the comparison of gridded and rainguage precipitation shows that gridded data highly overestimates the climatic station data. Similar results were observed in the comparison of flow simulated by SWAT model. The Peak flood calculated from JRA-55 overestimates while the Era-Interim peak floods are comparable to that of climatic stations in two of the three catchments.

Brazilian vehicular emission inventory software – BRAVES

• We present the BRAzilian Vehicular Emissions inventory Software – BRAVES. • The new method combines a top-down approach with county vehicular information. • The model calculates emissions by fleet category and emission process at county scale. • Local emissions patterns are captured by our new model. • Our model is easily adapted to other developing countries and data scarce regions. In this work, we present the BRAzilian Vehicular Emissions inventory Software - BRAVES which combines a bottom-up method with fleet information of each county. The new model uses a probabilistic approach that accounts for the fleet characteristics, fuel consumption, vehicle deterioration, and intensity of use, to calculate the vehicular emissions from the exhaust, tires, roads, brakes wear, soil resuspension, refueling, and evaporative emissions. Brazil is the fourth largest vehicle producer in the world. However, there is a lack of detailed information on vehicular emissions, especially in middle and small counties. BRAVES can estimate emissions of a larger variety of pollutants at the county scale and segregate the emissions by formation process and fleet category, a major advantage over national and state inventories. BRAVES is a compromise of more sophisticated models that provide detailed information in data-scarce areas, like small counties and could be easily adapted to other developing countries.

The clam and the dam: A Bayesian belief network approach to environmental flow assessment in a data scarce region

The Volta clam, Galatea paradoxa, is a freshwater macrobenthic bivalve which is endemic to the Lower Volta River in Ghana. The range of occurrence of the clam has been influenced by the flow regime in the Lower Volta which is in turn controlled by operation of two dams located upstream. Previous research has documented the changes to the Lower Volta due to the dams and attempts have been made to design environmental flows (e-flows), freshwater flows to sustain ecosystems, to inform the re-operation of the dams. The past attempts were based on the pre-dam, natural flow regime of the Lower Volta. In this study, a designer e-flow approach is explored using the Volta clam as an indicator species. Using knowledge garnered from various sources on the lifecycle, habitat and the local conditions corresponding to historical and current states of the Volta clams, the factors influencing its extent are visualized and quantified in a Bayesian belief network (BBN). Based on this BBN, an e-flow recommendation for the Lower Volta is for low flows, between 50 m3/s and 330 m3/s, for four months during the Volta clam veliger larva and recruitment life stages which occur in November to March. In addition, it is recommended that full breaching of the sandbar which regularly builds up at the Volta Estuary is done annually and that sand winning on the river bed is prohibited. These e-flow and management recommendations will have consequences for other water users and these have to be investigated, for instance by flow experiments and trade-off analysis. The results show that a BBN is potentially suitable for modelling the linkages between flows, management practices and the status of ecological indicators for the development of e-flows for highly modified rivers in data scarce regions.

A flood assessment of data scarce region using an open-source 2D hydrodynamic modeling and Google Earth Image: a case of Sabarmati flood, India

A flood assessment of data scarce region using an open-source 2D hydrodynamic modeling and Google Earth Image: a case of Sabarmati flood, India

Integrated Geospatial Analysis and Hydrological Modeling for Peak Flow and Volume Simulation in Rwanda

The ability to adequately and continually assess the hydrological catchment response to extreme rainfall events in a timely manner is a prerequisite component in flood-forecasting and mitigation initiatives. Owing to the scarcity of data, this particular subject has captured less attention in Rwanda. However, semi-distributed hydrological models have become standard tools used to investigate hydrological processes in data-scarce regions. Thus, this study aimed to develop a hydrological modeling system for the Nyabarongo River catchment in Rwanda, and assess its hydrological response to rainfall events through discharged flow and volume simulation. Initially, the terrain Digital Elevation Model (DEM) was pre-processed using a geospatial tool (HEC-GeoHMS) for catchment delineation and the generation of input physiographic parameters was applied for hydrological modeling system (HEC-HMS) setup. The model was then calibrated and validated at the outlet using sixteen events extracted from daily hydro-meteorological data (rainfall and flow) for the rainy seasons of the country. More than in other events, the 15th, 9th, 13th and 5th events showed high peak flows with simulated values of 177.7 m3s−1, 171.7 m3s−1, 169.9 m3s−1, and 166.9 m3s−1, respectively. The flow fluctuations exhibited a notable relation to rainfall variations following long and short rainy seasons. Comparing the observed and simulated hydrographs, the findings also unveiled the ability of the model to simulate the discharged flow and volume of the Nyabarongo catchment very well. The evaluated model’s performance exposed a high mean Nash Sutcliffe Efficiency (NSE) of 81.4% and 84.6%, with correlation coefficients (R2) of 88.4% and 89.8% in calibration and validation, respectively. The relative errors for the peak flow (5.5% and 7.7%) and volume (3.8% and 4.6%) were within the acceptable range for calibration and validation, respectively. Generally, HEC-HMS findings provided a satisfactory computing proficiency and necessitated fewer data inputs for hydrological simulation under changing rainfall patterns in the Nyabarongo River catchment. This study provides an understanding and deepening of the knowledge of river flow mechanisms, which can assist in establishing systems for river monitoring and early flood warning in Rwanda.

Application of the group method of data handling (GMDH) approach for landslide susceptibility zonation using readily available spatial covariates

Landslide susceptibility (LS) mapping is an essential tool for landslide risk assessment. This study aimed to provide a new approach with better performance for landslide mapping and adopting readily available variables. In addition, it investigates the capability of a state-of-the-art model developed using the group method of data handling (GMDH) to spatially model LS. Furthermore, hybridized models of GMDH were developed using different metaheuristic algorithms. The study area was the Bonghwa region of South Korea, for which an accurate landslide inventory dataset is available. We considered a total of 13 spatial covariates (altitude, slope, aspect, topographic wetness index, valley depth, plan curvature, profile curvature, distance from fault, distance from river, distance from road, land use, density of forest, and lithology were chosen as independent variables). Two benchmark models—random forest and boosted regression trees—were used to compare their results with the standalone GMDH and hybridized models. We compared model accuracy using the two most robust evaluation metrics, root mean square error (RMSE) and area under the receiver operating characteristic curve (AUROC). The validation results showed that hybridized models outperformed the standalone GMDH model. Moreover, the hybridized GMDH-PSO (AUC = 0.83, RMSE = 0.108), GMDH-IWO (AUC = 0.81, RMSE = 0.111), GMDH-BBO (AUC = 0.8; RMSE = 0.12), and GMDH-ICA (AUC = 0.8; RMSE = 0.117) had a better predictive performance than both RF and BRT. Therefore, the proposed approach could successfully produce landslide susceptibility maps using relatively few readily available variables and can be repeated in data-scarce regions.

Geomorphology-Wavelet based approach to rainfall runoff modeling for data scarce semi-arid regions, Kolar River catchment, India

The conceptual and physical mathematical model of rainfall-runoff modeling uses various parameters such as land use land cover, soil type classification, rainfall, atmospheric data such as temperature, evapotranspiration, solar radiation and wind speed, etc. But these data may not be available for developing countries and data scares semi-arid watershed. Also, the problem is even more critical for ungauged catchments and where manual record is maintained of water level and rainfall data. To address this issue, trend analysis is performed using Mann-Kendall test and Sen’s slope test which shows significant trend change stressing the need for new method for runoff prediction for better water resource management. In this study, a total of four models namely nonlinear autoregressive model with exogenous inputs lumped (LNARX), nonlinear autoregressive model with exogenous geomorphometrically processed inputs (GNARX), wavelet nonlinear autoregressive model with exogenous inputs (WLNARX) and nonlinear autoregressive model with exogenous geomorphometrically processed inputs (WGNARX). Ten models with different input combinations were selected based on their performance are analyzed for all the four networks. The best performing model for these networks is model no. 6 with WGNARX network with NSE 0.97 and RMSE 0.97 and with least value of RMSE. This method can be applied to data scarce region where data available are available for shorter duration and helpful for ungauged catchments also.

Evaluation of Three Gridded Precipitation Products to Quantify Water Inputs over Complex Mountainous Terrain of Western China

This study evaluates the capacity of three gridded precipitation products (MSWEP V2.2, TRMM-3B42 V7, and GPM-IMERG V6) to detect precipitation in the Min Jiang watershed, a data-scarce and mountainous region in western China. A set of statistical and contingency indices is calculated for the precipitation products and compared with rain gauge observations at 23 ground stations from July 2000 to May 2016. Consistency between gridded and ground precipitation datasets is examined at different temporal (i.e., daily, monthly, seasonally, and annually) and spatial (i.e., site level, sub-regional level, and watershed level) resolutions. We identify possible reasons for discrepancies among precipitation datasets. Our results indicate that: (1) the MSWEP product is best suited for the study of long-term mesoscale rainfall, rather than short-term light or extreme rainfall; (2) the IMERG product represents stable performance for the simulation of rainfall spatial variability and detection capability; and (3) Composition of the datasets, climatic systems, and regional topography are key factors influencing the consistency between gridded and ground precipitation datasets. Therefore, we suggest using MSWEP V2.2 and GPM-IMERG V6 as potential precipitation data sources for hydrometeorological studies over the Min Jiang watershed. The findings of this study inform future hydrometeorological and climate applications in data-scarce regions with complex terrain.

GFPLAIN and Multi-Source Data Assimilation Modeling: Conceptualization of a Flood Forecasting Framework Supported by Hydrogeomorphic Floodplain Rapid Mapping

Hydrologic/hydraulic models for flood risk assessment, forecasting and hindcasting have been greatly supported by the rising availability of increasingly accurate and high-resolution Earth Observation (EO) data. EO-based topographic and hydrologic open geo data are, nowadays, available on large scales. Data Assimilation (DA) models allow Early Warning Systems (EWS) to produce accurate and timely flood predictions. DA-based EWS generally use river flow real-time observations and 1D hydraulic models to identify potential inundation hot spots. Detailed high-resolution 2D hydraulic modeling is usually not used in EWS for the computational burden and the numerical complexity of injecting multiple spatially distributed sources of flow observations. In recent times, DEM-based hydrogeomorphic models demonstrated their ability in characterizing river basin hydrologic forcing and floodplain domains providing data-parsimonious opportunities for data-scarce regions. This work investigates the use of hydrogeomorphic floodplain terrain processing for optimizing the ability of DA-based EWSs in using diverse distributed flow observations. A flood forecasting framework with novel applications of hydrogeomorphic floodplain processing is conceptualized for empowering flood EWSs in preliminarily identifying the computational domain for hydraulic modeling, rapid flood detection using satellite images, and filtering geotagged crowdsourced data for flood monitoring. The proposed flood forecasting framework supports the development of an integrated geomorphic-hydrologic/hydraulic modeling chain for a DA that values multiple sources of observation. This work investigates the value of floodplain hydrogeomorphic models to tackle the major challenges of DA for EWS with specific regard to the computational efficiency issues and the lack of data in ungauged river basins towards an improved flood forecasting able to use advanced hydrodynamic modeling and to inject all available sources of observations including flood phenomena captures by citizens.

Estimating water balance components and their uncertainty bounds in highly groundwater-dependent and data-scarce area: An example for the Upper Citarum basin

Study regionUpper region of the Citarum basin in Indonesia. Study focusAssessing water balance components in data-scarce regions using different approaches could result in different outcomes. In the upper reaches of the Citarum River in West Java, Indonesia, for example, many previous studies found the groundwater storage to be depleting, while GRACE identifies a contrasting trend of increasing groundwater storage change. Therefore, in this study, we aim to improve the accuracy of water balance components estimates in the Upper Citarum basin. Firstly, we estimate groundwater abstraction volumes based on population size and a review of literature. Estimates of the other water balance components, namely the rainfall, actual evaporation, discharge, and groundwater storage change are derived from various global datasets and available measurements. We also use a distributed hydrological model, wflow_sbm, to yield additional estimates of discharge and actual evaporation. We compare each basin water balance estimate and quantify the uncertainty of some components using the Extended Triple Collocation (ETC) method. New hydrological insights for the regionETC application on four different rainfall estimates suggests a preference of using CHIRPS product in the study area as it delivers r2 of 0.56 and RMSE of 6.52 mm/day, compared to estimates from rainfall station (r2=0.39, RMSE = 8.57 mm/day), SACA&D (r2=0.29, RMSE = 10.46 mm/day), and TRMM (r2=0.56, RMSE = 8.61 mm/day). With CHIRPS rainfall forcing, wflow_sbm model estimates of average daily actual evaporation and discharge are obtained. The results for actual evaporation (2.67 mm/day) are plausible with a narrow difference of less than 0.50 mm/day among other estimates. The simulated discharge results in a daily average of 5.38 mm/day, estimated between observation data (3.65 mm/day) and GloFAS-ERA5 product (6.12 mm/day). Combining precipitation, actual evaporation, and discharge with a groundwater abstraction estimate of 0.57 mm/day, the wflow_sbm-based groundwater storage change is estimated at a daily storage depletion of 0.82 mm/day. Using GLEAM actual evaporation estimates (3.13 mm/day) and observed daily discharge, on the other hand, results in surplus water of 0.45 mm/day for groundwater storage change. These results demonstrate the high uncertainty in capturing subsurface hydrological processes, although the groundwater storage change estimates are found close to the estimates based upon GRACE gravimetric satellite data of 0.25 mm/day, with a variance of 1.57 mm/day. To aid in estimating current and future basin-scale groundwater level changes to support operational water management and policy in the Citarum basin, considering the massive groundwater abstraction, a focus on subsurface hydrological components quantification is of great importance for future research.

Groundwater potential assessment using an integrated AHP-driven geospatial and field exploration approach applied to a hard-rock aquifer Himalayan watershed

Study RegionEastern Nepal, The Himalayas. Study focusThis research is aimed at delineating groundwater potential zones in a hard-rock aquifer Himalayan watershed integrating geo-spatial and field exploration techniques. A customized indicator-based weighting method, consisting of six indicators, was developed and applied in the study watershed. Spatially-distributed maps for the indicators were produced based on information from primary as well as secondary sources. The indicators were further aggregated and groundwater potential map was prepared using relative weights derived from Analytical Hierarchy Process with inputs from key hydrogeology experts. The map was then validated using observed data of springs as well as results from Electrical Resistivity Tomography (ERT). New hydrological insightsThe novelty of this study lies in the application of two-step validation (using springs data from the field and ERT) of the groundwater potential mapping approach which demonstrates the robustness of the overall methodology for data scarce regions and paves way for its wider applicability in decision support for groundwater exploration activities. Further, this is one of the very few groundwater potential studies in the Nepal Himalaya hydrogeological setting.

Natural and human-induced drivers of groundwater depletion in Wadi Zabid, Tihama coastal plain, Yemen

Groundwater depletion is a problem in many parts the world. We developed an approach to investigate the drivers of groundwater depletion in data-scarce regions. The approach combines natural and human-induced drivers, with the latter focusing on the link between human activities and government policies. We tested the approach in Wadi Zabid, Yemen. Forty years of rainfall-runoff data were analyzed, alongside changes in land cover, groundwater abstraction and related policies. No decrease in rainfall was observed, but runoff did decrease slightly. Significant expansion of agricultural lands led to increased demand for irrigation water, which was provided by drilling wells and building water harvesting/diversion structures. In Wadi Zabid, human activities, stimulated by policy measures, were the main drivers of groundwater depletion (water table here fell by 1 m/yr on average over 1972–2016). We conclude that combining natural and human-induced factors is indeed a valuable approach for investigating groundwater depletion drivers.

Assessment of bottom-up satellite rainfall products on estimating river discharge and hydrologic signatures in Brazilian catchments

Satellite rainfall products are one of the most valuable tools for water resources monitoring in data-scarce regions, due to their low latency and quasi-global range. However, there are still uncertainties associated with rainfall products performance used to estimate hydrologic signatures in several regions, such as Brazil. Here, we investigate the performance of three rainfall products in estimating daily precipitation, daily river discharge, and hydrologic signatures over Brazil: the SM2RAIN-ASCAT and the GPM+SM2RAIN satellite products, and the ERA5 reanalysis product. We used a subset of 520 catchments from the Catchments Attributes for Brazil (CABra) dataset and the hydrologic modeling was carried out using the MISDc hydrologic model. Satellite-based products performed better than ERA5 for most Brazilian biomes in estimating daily precipitation when compared with ground observations used as reference. Daily river discharge was also better modeled with SM2RAIN-ASCAT and GPM+SM2RAIN. Hydrologic modeling presented low values of bias and >80% of catchments with KGE > 0.5 in calibration. Lastly, hydrologic signatures were well estimated by SM2RAIN-ASCAT and GPM+SM2RAIN, and for some biomes (Atlantic Forest, Cerrado, and Caatinga) they are better predictors than ground-based observations. We showed that there is a significant added value when using SM2RAIN-ASCAT and GPM+SM2RAIN products in tropical catchments, allowing a high-quality continuous water resources monitoring even in data-scarce regions. Besides, our findings pave the way for a better understanding of hydrologic extremes (droughts and floods) using these satellite rainfall products on multiple spatial and temporal scales.

Hydrologic Regionalization under Data Scarcity: Implications for Streamflow Prediction

Continuous streamflow prediction is crucial in many applications of water resources planning and management. However, streamflow prediction is challenging, particularly in data-scarce regions. This paper demonstrates an approach to regionalize the flow duration curve for predicting daily streamflow in the data-scare region of the central Himalayas. We developed a regression-based model to estimate streamflow at various segments of a flow duration curve by incorporating basin characteristics and climate variables. This study analyzes the sensitivities of proximity and characteristics between the donor (gauged) and receptor (ungauged) basins for time-series streamflow prediction. Our results show that regionalization techniques perform better in low to medium flows over high flows. Our findings are significant in the central Himalayan regional context to inform operational and management decisions in water sector projects like hydropower plants, which generally rely on low-to-medium streamflow information. Although the quantitative results are region-specific, the approach and insights are generalizable to the Himalayan region.

Water environmental pressure assessment in agricultural systems in Central Asia based on an Integrated Excess Nitrogen Load Model

Agricultural runoff is the main source of water pollution in Central Asia. Excessive nitrogen (N) inputs from overuse of chemical fertilizers are threatening regional water resources. However, the scarcity of quantitative data and simplified empirical models limit the reliability of grey water footprint (GWF), particularly in undeveloped regions. In this study, we developed an Integrated Excess Nitrogen Load Model (IENLM) to calculate excess N load and evaluate its potential water environmental pressure in Central Asia. The model optimized the biological N fixation and atmospheric N deposition modules by involving more environmental variables and human activities. Results showed that N fertilizer application contributed over 60% to total N input and was mainly responsible for 42.9% increase of total GWF from 101.5 to 145.0 billion m3 during 1992 - 2018. Water pollution level (WPL) increased from 0.55 in 1992 to 2.41 in 2018 and the pollution assimilation capacity of water systems has been fully consumed just by N load from agriculture since 2005. GWF intensity and grey water pollution – efficiency types in all Central Asian countries have improved in recent years except for Turkmenistan. N fertilizer application and agricultural economy development were the main driving factors induced N pollution. Results were validated by riverine nitrate concentrations and the estimates from prior studies. In future, combining the N fertilizer reduction with other farm management practices were projected to effectively improve the WPL. The modeling framework is favorable for N pollution research in data-scarce regions and provides a scientific basis for decision-making for agriculture and water resource managements.

Simulating the hydrological regime of the snow fed and glaciarised Gilgit Basin in the Upper Indus using global precipitation products and a data parsimonious precipitation-runoff model

In many high altitude river basins, the hydro-climatic regimes and the spatial and temporal distribution of precipitation are little known, complicating efforts to quantify current and future water availability. Scarce, or non-existent, gauged observations at high altitudes coupled with complex weather systems and orographic effects further prevent a realistic and comprehensive assessment of precipitation. Quantifying the contribution from seasonal snow and glacier melt to the river runoff for a high altitude, melt dependent region is especially difficult. Global scale precipitation products, in combination with precipitation-runoff modelling may provide insights to the hydro-climatic regimes for such data scarce regions. In this study two global precipitation products; the high resolution (0.1° × 0.1°), newly developed ERA5-Land, and a coarser resolution (0.55° × 0.55°) JRA-55, are used to simulate snow/glacier melts and runoff for the Gilgit Basin, a sub-basin of the Indus. A hydrological precipitation-runoff model, the Distance Distribution Dynamics (DDD), requires minimum input data and was developed for snow dominated catchments. The mean of total annual precipitation from 1995 to 2010 data was estimated at 888 mm and 951 mm by ERA5-Land and JRA-55, respectively. The daily runoff simulation obtained a Kling Gupta efficiency (KGE) of 0.78 and 0.72 with ERA5-Land and JRA-55 based simulations, respectively. The simulated snow cover area (SCA) was validated using MODIS SCA and the results are quite promising on daily, monthly and annual scales. Our result showed an overall contribution to the river flow as about 26% from rainfall, 37–38% from snow melt, 31% from glacier melt and 5% from soil moisture. These melt simulations are in good agreement with the overall hydro-climatic regimes and seasonality of the area. The proxy energy balance approach in the DDD model, used to estimate snow melt and evapotranspiration, showed robust behaviour and potential for being employed in data poor basins.

Assessment of Future Water Yield and Water Purification Services in Data Scarce Region of Northwest China.

Water shortage and pollution have become prominent in the arid regions of northwest China, seriously affecting human survival and sustainable development. The Bosten Lake basin has been considered as an example of an arid region in northwest China, and the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model has been used to quantitatively evaluate the future water yield and water purification services for four representative concentration pathways (RCP) scenarios. The results show that for the four RCP scenarios, the annual average precipitation in 2020–2050 decreases compared to that in 1985–2015; the area of cultivated land and unused land decreases, and the area of other land-use types increases from 2015 to 2050. The water yield service reduces, while the water purification service increases from 2015 to 2050 in the Bosten Lake basin. In 2050, the water yield and water purification services are the best for the RCP6.0 scenario, and are the worse for the RCP4.5 scenario and RCP8.5 scenario, respectively. The distribution of the water yield and water purification services show a gradual decline from northwest to southeast.

Runoff monitoring in the Lhasa River Basin using passive microwave data

Monitoring runoff is critical for water resources management. Remote sensing can be an effective technique to investigate changes in runoff on data-scarce regions. This study used Calibrated Passive Microwave Daily EASE-Grid 2.0 Brightness Temperature Earth System Data Record (CETB) and M/C signal method to simulate runoff in Lhasa River Basin. This satellite-derived runoff was compared with gauge observations and Soil and Water Assessment Tool (SWAT) model simulations. The results show that (1) the well-correlated satellite-derived M/C signals are able to detect the runoff process in the selected river channels. For most gauges, the runoff generated by M/C signals fit well with observation, giving a Pearson correlation coefficient greater than 0.9, Nash-Sutcliffe efficiency coefficient greater than 0.8, and relative error less than 15%; (2) When compared to the SWAT model, the M/C signal method has advantages in flood season runoff simulation. These two simulation methods are nearly equal for the entire runoff process; (3) For runoff simulation influenced by the underlying surface and the shape of the river channel, the M/C signal method is better suited in areas with a low fraction vegetation cover and channels with a typical inverted trapezoid shape. Overall, we conclude that M/C signal method simulation is a valuable source of surface water information and could be used for runoff monitoring in data-scarce areas.

How well do gridded precipitation and actual evapotranspiration products represent the key water balance components in the Nile Basin?

Study regionNile Basin, Africa. Study focusThe accurate representation of precipitation (P) and actual evapotranspiration (ETa) patterns is crucial for water resources management, yet there remains a high spatial and temporal variability among gridded products, particularly over data-scarce regions. We evaluated the performance of eleven state-of-the-art P products and seven ETa products over the Nile Basin using a four-step procedure: (i) P products were evaluated at the monthly scale through a point-to-pixel approach; (ii) streamflow was modelled using the Random Forest machine learning technique, and simulated for well-performing catchments for 2009–2018 (to correspond with ETa product availability); (iii) ETa products were evaluated at the multiannual scale using the water balance method; and (iv) the ability of the best-performing P and ETa products to represent monthly variations in terrestrial water storage (ΔTWS) was assessed through a comparison with GRACE Level-3 data. New hydrological insights for the regionCHIRPSv2 was the best-performing P product (median monthly KGE’ of 0.80) and PMLv2 and WaPORv2.1 the best-performing ETa products over the majority of the evaluated catchments. The application of the water balance using these best-performing products captures the seasonality of ΔTWS well over the White Nile Basin, but overestimates seasonality over the Blue Nile Basin. Our study demonstrates how gridded P and ETa products can be evaluated over extremely data-scarce conditions using an easily transferable methodology.