Data-scarce Basin Research Articles

How do gridded meteorological datasets perform in a typical data-scarce cryospheric basin?

Gridded meteorological datasets have been widely used in meteorological and hydrological studies, especially in data-scarce regions. However, assessments of these datasets in cryospheric basins, which are highly sensitive to global warming, remain inadequate. Therefore, three representative gridded meteorological datasets, i.e., the China meteorological forcing dataset (CMFD), CN05.1, and the meteorological forcing dataset for the third-pole region (TPMFD), were systematically evaluated in the Nachitai Basin based on comparisons with limited gauge observations, pairwise intercomparisons, and hydrological simulations. All gridded precipitation and temperature datasets were in good agreement with gauge observations on an intra-annual scale, while substantial differences existed among them in terms of precipitation and temperature on an annual scale. Additionally, pairwise correlation analyses indicated that the temporal consistency between the datasets was higher than their spatial consistency. Furthermore, all three datasets achieved satisfactory and reasonable results in simulating glacier area and streamflow variations when used as inputs for the hydrological−hydrodynamic model, exhibiting the robustness of the model in this data-scarce basin. This study provides a more profound understanding of the significance of gridded datasets in data-scarce areas situated on the Tibetan Plateau.

An improved nonlinear dynamical model for monthly runoff prediction for data scarce basins

An improved nonlinear dynamical model for monthly runoff prediction for data scarce basins

Evaluation of the support vector regression (SVR) and the random forest (RF) models accuracy for streamflow prediction under a data-scarce basin in Morocco

Streamflow prediction is a key variable for water resources management. It becomes more important in semi-arid regions such as the Tensift river basin in Morocco, where water resources are facing a severe drought and the demand is continuously increasing. The present analysis focuses on evaluating Machine Learning techniques, namely support vector regression (SVR) and Random Forest (RF) against the multiple linear regression (MLR) for daily streamflow forecasting in the mountainous sub-basin of Rheraya between 2003 and 2016. The results show that SVR performed best, followed by RF and MLR. In measurable terms and regarding mean performance, SVR exhibited the higher Nash–Sutcliffe efficiency score (NSE = 0.59) and a lower root mean squared error (RMSE = 1.18 m3s-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext {m}^3\\,\ ext {s}^{-1}$$\\end{document}) compared to RF (NSE = 0.53, RMSE = 1.18 m3s-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext {m}^3\\,\ ext {s}^{-1}$$\\end{document}) and MLR (NSE = 0.54, RMSE = 1.01 m3s-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext {m}^3\\,\ ext {s}^{-1}$$\\end{document}). Furthermore,the available time series was too short to properly capture the full range of streamflow variability, which reduced the prediction performance outside of the calibration conditions. These findings suggest that ML algorithms, particularly SVR, can provide accurate streamflow estimation useful for water resources management when trained on a representative period. The results highlight the capacity of Machine Learning algorithms, specifically SVR, to augment streamflow prediction for enhanced water resource management in arid regions.

Transfer learning framework for streamflow prediction in large-scale transboundary catchments: Sensitivity analysis and applicability in data-scarce basins

Transfer learning framework for streamflow prediction in large-scale transboundary catchments: Sensitivity analysis and applicability in data-scarce basins

Modeling climate change impacts on blue and green water of the Kobo-Golina River in data-scarce upper Danakil basin, Ethiopia

Study regionKobo-Golina River, Upper Danakil Basin, Ethiopia. Study focusesIt is crucial to understand the spatiotemporal distribution of blue water (BW) and green water (GW) for optimal use of water resources, especially in data-scarce regions. This study aims to evaluate the extent to which future climate is changing, and its impact on blue-green water resources in the study area. Projected changes were predicted based on the latest Coupled Model Intercomparison Project Phase 6 (CMIP6) Global Climate Models (GCMs) for three future periods (2015–2044, 2045–2075, 2076–2100) under two shared socio-economic pathways (SSP2–4.5 & SSP5–8.5). Compromise programming technique was employed to rank and select best performing GCMs. The multi-variable calibrated SWAT+ model was forced with climate projections from the top-ranked CMIP6 GCMs ensemble to simulate projected blue water (BW) and green water (GW) in the study area. New hydrological insights for the regionCompared to the baseline period (1984–2014), blue water declined while green water exhibited an increasing trend in all future periods under two SSPs. It is also noted that the spatial distribution of BW and GW remains uneven in the study area. Precipitation significantly impacted BW than GW resources. This study provides valuable insights into the utilization of the recent CMIP6 Global Climate Model coupled with multi-variable calibrated SWAT+ hydrological models for better simulation of Blue-Green water in data-scarce basins under changing climate.

Assessing Recession Constant Sensitivity and Its Interaction with Data Adjustment Parameters in Continuous Hydrological Modeling in Data-Scarce Basins: A Case Study Using the Xinanjiang Model

Data scarcity plays the crucial role in hydrological modeling, causing the uncertainties in hydrological model calibration and parameterization. Therefore, while considering the sensitivity of the parameter optimization, it is essential to determine which parameters have the most significant implications on model performance, especially when there is limited hydro-climatological information. Previous studies have underscored the significance of data adjustment parameter sensitivity and its consequential influence on both Xinanjiang (XAJ) model performance and the determination of the acceptable minimum data length, particularly in data-scarce regions. Nevertheless, it is essential to consider the recession constant sensitivity as it has been identified as the most sensitive parameter on an annual scale while keeping the data adjustment parameters constant during a period of data scarcity. Hence, the objective of this study is to extend the previous research by examining the relationship between recession constant sensitivity and data adjustment parameters in shorter datasets leading to more reliable parameter estimation for data-scarce basins. Five U.S. river basins were analyzed using the 28-year dataset and shorter subsets to highlight the impacts of recession constant sensitivities with different data lengths. This study explores the impact of recession constant sensitivities over the hydrological parameter estimation using two approaches (cg): (i) assessing the relationship between the recession constant (cg) and the data adjustment parameter (Cep), for the 28-year dataset, and (ii) investigating the significant impacts of the sensitivity of cg over Cep in shorter datasets, which can affect the estimation of the acceptable minimum data length in the data-scarce basins. The polynomial regression analysis was applied to compare and evaluate the model results, varying over the recession constant with different data lengths. The findings indicated that the influence of the recession constant over the data adjustment parameters in the 28-year dataset is limited in the annual scale. However, there is a significant impact of recession constant sensitivity over the model performance while calibrating the model with subsets, particularly in the worst-case scenario. This study underscores the importance of the recession constant sensitivity for reliable continuous hydrological model predictions, especially in data-scarce areas.

Ungauged Basin Flood Prediction Using Long Short-Term Memory and Unstructured Social Media Data

Floods are highly perilous and recurring natural disasters that cause extensive property damage and threaten human life. However, the paucity of hydrological observational data hampers the precision of physical flood models, particularly in ungauged basins. Recent advances in disaster monitoring have explored the potential of social media as a valuable source of information. This study investigates the spatiotemporal consistency of social media data during flooding events and evaluates its viability as a substitute for hydrological data in ungauged catchments. To assess the utility of social media as an input factor for flood prediction models, the study conducted time-series and spatial correlation analyses by employing spatial scan statistics and confusion matrices. Subsequently, a long short-term memory model was used to forecast the outflow volume in the Ui Stream basin in South Korea. A comparative analysis of various input factor combinations revealed that datasets incorporating rainfall, outflow models, and social media data exhibited the highest accuracy, with a Nash–Sutcliffe efficiency of 94%, correlation coefficient of 97%, and a minimal normalized root mean square error of 0.92%. This study demonstrated the potential of social media data as a viable alternative for data-scarce basins, highlighting its effectiveness in enhancing flood prediction accuracy.

Universal recession constants and their potential to predict recession flow

Streamflow prediction during dry or recession periods is crucial for water resources management. The storage-discharge (S-Q) relationship is generally used to model recession flow in the basin. Since storage is practically unobservable, the S-Q relationship is often quantified by analyzing the relationship between -dQ/dt and Q, which Brutsaert and Nieber (1977) observed to be of a power-law type: -dQ/dt=kQα, for natural basins. Integration of the above equation yields an expression for the streamflow at any time, t, during the recession period. However, this approach relies on the availability of the streamflow data and, therefore, cannot be applied directly to ungauged or data-scarce basins. Thus, there exists a necessity for an alternative approach to predict streamflow in the ungauged basins during the recession period. In this study, we investigated recession curves from 413 basins in the United States and determined the existence of a universal relationship (k=kuQN-λu) between the storage coefficient (k) and the average past discharge (QN) that can serve as a proxy for basin storage. We tested the potential of universal recession constants (ku and λu) in predicting the recession flow in both gauged and ungauged basins. It is observed that the median NSEBC, i.e., NSE calculated using the Box-Cox transformation of discharge, was 0.78 for the gauged basins and 0.55 for the ungauged basins. Our findings provide a model-independent method for predicting the recession discharge, which can pave new avenues towards improving the predictions in ungauged basins.

Analysing the elevation-distributed hydro-climatic regime of the snow covered and glacierised Hunza Basin in the upper Indus

In the high altitude Hindukush Karakoram Himalaya (HKH) mountains, the complex weather system, inaccessible terrain and sparse measurements make the elevation-distributed precipitation and temperature among the most significant unknowns. The elevation-distributed snow and glacier dynamics in the HKH region are also little known, leading to serious concerns about the current and future water availability and management. The Hunza Basin in the HKH region is a scarcely monitored, and snow- and glacier-dominated part of the Upper Indus Basin (UIB). The current study investigates the elevation-distributed hydrological regime in the Hunza Basin. The Distance Distribution Dynamics (DDD) model, with a degree day and an energy balance approach for simulating glacial melt, is forced with precipitation derived from two global datasets (ERA5-Land and JRA-55). The mean annual precipitation for 1997–2010 is estimated as 947 and 1,322 mm by ERA5-Land and JRA-55, respectively. The elevation-distributed precipitation estimates showed that the basin receives more precipitation at lower elevations. The daily river flow is well simulated, with KGE ranging between 0.84 and 0.88 and NSE between 0.80 and 0.82. The flow regime in the basin is dominated by glacier melt (45%–48%), followed by snowmelt (30%–34%) and rainfall (21%–23%). The simulated snow cover area (SCA) is in good agreement with the MODIS satellite-derived SCA. The elevation-distributed glacier melt simulation suggested that the glacial melt is highest at the lower elevations, with a maximum in the elevation 3,218–3,755 masl (14%–21% of total melt). The findings improve the understanding of the local hydrology by providing helpful information about the elevation-distributed meltwater contributions, water balance and hydro-climatic regimes. The simulation showed that the DDD model reproduces the hydrological processes satisfactorily for such a data-scarce basin.

Strategic siting and design of dams minimizes impacts on seasonal floodplain inundation

Dams and reservoirs aid economic development but also create significant negative impacts. Dams fragment rivers and reduce longitudinal connectivity on a network scale. However, dams may also alter discharge regimes and flood peaks, consequently reducing floodplain inundation and lateral channel floodplain connectivity, which impacts floodplain associated ecosystems. Strategic planning has emerged as a promising approach to find a balance between dam impacts and benefits. Yet, strategic planning has predominantly focused on longitudinal connectivity due to the difficulty of including the complex interactions between dam design and operations, hydrologic regime alteration, and the hydrodynamic processes controlling downstream flood extent. Here, we present how to reduce conflicts between hydropower development and loss of floodplain inundation extent by jointly optimizing siting and design of many dams in a data scarce basin. We deploy a coupled hydrological—hydraulic simulation model linked to a multiobjective optimization framework to find development options with the least trade-offs between power generation and downstream impacts on floodplains. Our results for the Pungwe Basin in Mozambique indicate that whilst portfolios of many small storage and run-of-river diversion hydropower plants might create less impacts on the downstream floodplains, installation of some large storage dams would be necessary to attain higher levels of hydropower generation.

Insights from a comparison of two hydrological modelling approaches in the Kwando (Cuando) River and the western tributaries of the Zambezi River basin

Study Region: The Kwando (Cuando) River and the western headwaters of the Zambezi River, which are data-scarce basins of southern Africa. Study Focus: A comparative analysis of the performance of two fundamentally different hydrological modelling approaches (a conceptual model and a theory guided machine learning model) in a data-sparse region. New Hydrological Insights for the Region: The machine learning model (HydroForecast) generally performs better – in terms of statistical fit between simulated and observed flows – than the conceptual model (Pitman). For the Kwando River, the conceptual model explicitly simulates the expected attenuation effects of a large floodplain, while the machine learning model represents this and other processes implicitly. The two models quantify the Kwando sub-basin flow contributions differently, with the conceptual model calibrated manually to align with the available qualitative information that suggests that the majority of the runoff is generated in the upstream sub-basin and then attenuated in the downstream floodplain. Generally, this work offers insight into how the two very different models can simulate historical flows in a large basin when streamflow observations and the forcing rainfall data are limited and of unknown quality, and suggests that a machine learning model better leverages information from multiple training parameters to reproduce the measured streamflows.

Modeling dissolved and particulate organic carbon dynamics at basin and sub-basin scales

Dissolved organic carbon (DOC) and particulate organic carbon (POC) play a fundamental role in biogeochemical cycles of freshwater ecosystems. However, the lack of readily available distributed models for carbon export has limited the effective management of organic carbon fluxes from soils, through river networks and to receiving marine waters. We develop a spatially semi-distributed mass balance modeling approach to estimate organic carbon flux at a sub-basin and basin scales, using commonly available data, to allow stakeholders to explore the impacts of alternative river basin management scenarios and climate change on riverine DOC and POC dynamics. Data requirements, related to hydrological, land-use, soil and precipitation characteristics are easily retrievable from international and national databases, making it appropriate for data-scarce basins. The model is built as an open-source plugin for QGIS and can be easily integrated with other basin scale decision support models on nutrient and sediment export.We tested the model in Piave river basin, in northeast Italy. Results show that the model reproduces spatial and temporal changes in DOC and POC fluxes in relation to changes in precipitation, basin morphology and land use across different sub-basins. For example, the highest DOC export were associated with both urban and forest land use classes and during months of elevated precipitation. We used the model to evaluate alternative land use scenarios and the impact of climate on basin level carbon export to Mediterranean.

Downscaling the Z-R relationship and bias correction solution for flash flood assessment in a data-scarce basin, Thailand.

Weather radar is a form of alternative indirect rainfall measurement for use in mitigating flash flood hazards. It is a challenging task to obtain accurate radar rainfall data without integration with automatic rain gauge networks. This paper investigated transformation equations to convert the calibrated daily Z-R relationship to the sub-hourly scale and proposed optional schemes for downscaling the daily bias adjustment factor into 15 min resolution scale to produce a high-resolution radar rainfall product for flash flood modelling. Radar reflectivity data from three radar stations in Thailand and their corresponding daily gauge rainfall data were used in the analysis. Two bias adjustment schemes (DMFB and DS_DMFB), accounting for the temporal variation, and one spatiotemporal scheme (SPTB_IDS) were used to generate three corresponding rainfall datasets for the unified river basin simulator (URBS) model to simulate flood hydrographs in the Tubma basin, Thailand. The results showed that combining the proposed 15-min Z-R scaling equation and the SPTB_IDS produced the most reliable radar rainfall amount leading to an increase in the accuracy of flood modelling with the lowest uncertainty. This indicated that the temporal downscaling solution together with spatial interpolation technique for sub-hourly radar rainfall assessment could benefit flash flood simulation in a data-scarce basin.

Machine learning based groundwater prediction in a data-scarce basin of Ghana

ABSTRACT Groundwater (GW) is a key source of drinking water and irrigation to combat growing food insecurity and for improved water access in rural sub-Saharan Africa. However, there are limited studies due to data scarcity in the region. New modeling techniques such as Machine learning (ML) are found robust and promising tools to assess GW recharge with less expensive data. The study utilized ML technique in GW recharge prediction for selected locations to assess sustainability of GW resources in Ghana. Two artificial neural networks (ANN) models namely Feedforward Neural Network with Multilayer Perceptron (FNN-MLP) and Extreme Learning Machine (FNN-ELM) were used for the prediction of GW using 58 years (1960–2018) of GW data. Model evaluation between FNN-MLP and FNN-ELM showed that the former approach was better in predicting GW with R2 ranging from 0.97 to 0.99 while the latter has an R2 between 0.42 to 0.68. The overall performance of both models was acceptable and suggests that ANN is a useful forecasting tool for GW assessment. The outcomes from this study will add value to the current methods of GW assessment and development, which is one of the pillars of the sustainable development goals (SDG 6).

Quantitative analysis of the human intervention impacts on hydrological drought in the Zayande-Rud River Basin, Iran

Abstract In a human-dominated world, access to sustainable water resources has led to complex management policies that affect hydrological droughts. Applying the best approach to assess the contribution of these human-made changes to hydrological droughts is still underexplored. In this study, the individual and joint impacts of dam and inter-basin water transfer projects are quantified for the characteristic changes of hydrological drought using a developed data-based framework and were tested in a semi-arid, data-scarce basin in central Iran. The proposed data-based framework combines the upstream–downstream comparison method and the individual–station–drought analysis. This framework could properly assess the individual and joint contributions of dam and water transfer projections to making aggravations or alleviations in hydrological drought. It identified the dam and joint impacts of the dam with water transfer by 66 and 55%, respectively, as the most effective human intervention to alleviate the duration of hydrological drought. The proposed framework gives the flexibility to form different comparative analyses by using different types of flow data to assess the impacts of human interventions. This framework is also applicable in other regions to quantify the contributions of point-based human interventions to hydrological droughts. The comprehensive knowledge of solutions to alleviate the adverse impacts of droughts can reduce the damage in water-stressed regions.

A study on availability of ground observations and its impacts on bias correction of satellite precipitation products and hydrologic simulation efficiency

Precipitation is a crucial input for hydrological models to achieve various purposes such as water resource management and flood forecasting. However, precipitation stations are usually limited in number and eccentric in location. Although satellite precipitation products (SPPs) have improved remarkably, their application to data-sparse areas for flow simulation has significant limitations due to insufficient ground observations for bias correction. This paper proposes an integrated methodology from precipitation to flow simulation in a data-scarce basin by studying the impact of a precipitation gauge network on average areal precipitation (AAP) estimation, SPPs correction, and resultant hydrological simulation efficiency. To this end, seventeen observation network patterns were configured from a data-sufficient basin, the Fuji River Basin (FRB), Japan. Four bias correction methods were conducted for six SPPs under seventeen configurations of gauge network. A total of 264 AAP and 346 discharge results were comprehensively evaluated in combination with various gauge network configurations, bias correction methods, and hydrological simulation schemes. We found that, on bias correction methods, the proposed dynamic use of a statistical bias correction method (SDBC) can be the most effective, significantly improving the accuracy of SPPs even with a small number of eccentrically set observation stations. This study also revealed that four precipitation stations in the FRB (around 900 km2/station) could work as well as networks with more than ten or twenty stations for AAP estimation and flow simulation at the outlet. In addition, it is confirmed that the hydrological model could be well-calibrated individually by different precipitation inputs to fit the precipitation pattern. Those findings provide a robust basis for the use of SPPs in many poorly gauged basins. Also, the results on the configuration of networks provide scientific suggestions for properly deploying ground observation networks in ungauged basins for the effective use of SPPs.

Modeling Biological Oxygen Demand Load Capacity in a Data-Scarce Basin with Important Anthropogenic Interventions

Most water bodies are currently used as receptors for pollutants coming mainly from the industrial and domestic sectors. The Biobío river is subjected to multiple anthropogenic pressures such as industrial water supply, drinking water, hydroelectric power generation, agriculture, and the final receptor body of a large amount of industrial and urban waste, pressures that will intensify due to the decrease in water flow as a result of climate change. In this context, organic contamination has been found mainly from sewage discharges and oxidizable waste discharges generated by industrial processes. In this sense, the objective of this research is to determine the Biological Oxygen Demand Loading Capacity (LC) in a basin with a low density of water quality data subjected to strong anthropogenic pressures. To estimate the carrying capacity in a section of the Biobío River, the water quality model River and Stream Water Quality Model- Qual2K version 2.11b8, developed by Chapra, was used. This model solves the Streeter–Phelps equation, proposing an analytical expression to relate the dissolved oxygen (DO) and biochemical oxygen demand (BOD5) variables. These variables were modeled for different critical scenarios of minimum flows in return periods of 5, 50, and 100 years, determining that the studied section of the Biobío river would have a high carrying capacity to not be affected by its organic matter pollution.

Streamflow simulation in data-scarce basins using Bayesian and physics-informed machine learning models

AbstractHydrologic predictions at rural watersheds are important but also challenging due to data shortage. Long Short-TermMemory (LSTM) networks are a promising machine learning approach and have demonstrated good performance in streamflow predictions. However, due to its data-hungry nature, most of LSTM applications focused on well-monitored catchments with abundant and high quality observations. In this work, we investigate predictive capabilities of LSTM in poorly monitored watersheds with short observation records. To address three main challenges of LSTM applications in data-scarce locations, i.e., overfitting, uncertainty quantification (UQ), and out-of-distribution prediction, we evaluate different regularization techniques to prevent overfitting, apply a Bayesian LSTM for UQ, and introduce a physics-informed hybrid LSTM to enhance out-of-distribution prediction. Through case studies in two diverse sets of catchments with and without snow influence, we demonstrate that: (1) when hydrologic variability in the prediction period is similar to the calibration period, LSTM models can reasonably predict daily streamflow with Nash-Sutcliffe efficiency above 0.8, even with only two years of calibration data. (2) When the hydrologic variability in the prediction and calibration periods is dramatically different, LSTM alone does not predict well, but the hybrid model can improve the out-of-distribution prediction with acceptable generalization accuracy. (3) L2 norm penalty and dropout can mitigate overfitting, and Bayesian and hybrid LSTM have no overfitting. (4) Bayesian LSTM provides useful uncertainty information to improve prediction understanding and credibility. These insights have vital implications for streamflow simulation in watersheds where data quality and availability are a critical issue.

Performance evaluation of multiple satellite rainfall products for Dhidhessa River Basin (DRB), Ethiopia

Abstract. Precipitation is a crucial driver of hydrological processes. Ironically, a reliable characterization of its spatiotemporal variability is challenging. Ground-based rainfall measurement using rain gauges is more accurate. However, installing a dense gauging network to capture rainfall variability can be impractical. Satellite-based rainfall estimates (SREs) could be good alternatives, especially for data-scarce basins like in Ethiopia. However, SRE rainfall is plagued with uncertainties arising from many sources. The objective of this study was to evaluate the performance of the latest versions of several SRE products (i.e., CHIRPS2, IMERG6, TAMSAT3 and 3B42/3) for the Dhidhessa River Basin (DRB). Both statistical and hydrological modeling approaches were used for the performance evaluation. The Soil and Water Analysis Tool (SWAT) was used for hydrological simulations. The results showed that whereas all four SRE products are promising to estimate and detect rainfall for the DRB, the CHIRPS2 dataset performed the best at annual, seasonal and monthly timescales. The hydrological simulation-based evaluation showed that SWAT's calibration results are sensitive to the rainfall dataset. The hydrological response of the basin is found to be dominated by the subsurface processes, primarily by the groundwater flux. Overall, the study showed that both CHIRPS2 and IMERG6 products could be reliable rainfall data sources for the hydrological analysis of the DRB. Moreover, the climatic season in the DRB influences rainfall and streamflow estimation. Such information is important for rainfall estimation algorithm developers.

Integrating Satellite Rainfall Estimates with Hydrological Water Balance Model: Rainfall-Runoff Modeling in Awash River Basin, Ethiopia

Hydrologic models play an indispensable role in managing the scarce water resources of a region, and in developing countries, the availability and distribution of data are challenging. This research aimed to integrate and compare the satellite rainfall products, namely, Tropical Rainfall Measuring Mission (TRMM 3B43v7) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), with a GR2M hydrological water balance model over a diversified terrain of the Awash River Basin in Ethiopia. Nash–Sutcliffe efficiency (NSE), percent bias (PBIAS), coefficient of determination (R2), and root mean square error (RMSE) and Pearson correlation coefficient (PCC) were used to evaluate the satellite rainfall products and hydrologic model performances of the basin. The satellite rainfall estimations of both products showed a higher PCC (above 0.86) with areal observed rainfall in the Uplands, the Western highlands, and the Lower sub-basins. However, it was weakly associated in the Upper valley and the Eastern catchments of the basin ranging from 0.45 to 0.65. The findings of the assimilated satellite rainfall products with the GR2M model exhibited that 80% of the calibrated and 60% of the validated watersheds in a basin had lower magnitude of PBIAS (<±10), which resulted in better accuracy in flow simulation. The poor performance with higher PBIAS (≥±25) of the GR2M model was observed only in the Melka Kuntire (TRMM 3B43v7 and PERSIANN-CDR), Mojo (PERSIANN-CDR), Metehara (in all rainfall data sets), and Kessem (TRMM 3B43v7) watersheds. Therefore, integrating these satellite rainfall data, particularly in the data-scarce basin, with hydrological data, generally appeared to be useful. However, validation with the ground observed data is required for effective water resources planning and management in a basin. Furthermore, it is recommended to make bias corrections for watersheds with poorlyww performing satellite rainfall products of higher PBIAS before assimilating with the hydrologic model.