Integrating Global Satellite-Derived Data Products as a Pre-Analysis for Hydrological Modelling Studies: A Case Study for the Red River Basin

Precipitation and Soil Moisture Spatio-Temporal Variability and Extremes over Vietnam (1981-2019): Understanding Their Links to Rice Yield

Vietnam, one of the three leading rice producers globally, has recently seen an increased threat to its rice production emanating from climate extremes (floods and droughts). Understanding spatio-temporal variability in precipitation and soil moisture is essential for policy formulations to adapt and cope with the impacts of climate extremes on rice production in Vietnam. Adopting a higher-order statistical method of independent component analysis (ICA), this study explores the spatio-temporal variability in the Climate Hazards Group InfraRed Precipitation Station’s (CHIRPS) precipitation and the Global Land Data Assimilation System’s (GLDAS) soil moisture products. The results indicate an agreement between monthly CHIRPS precipitation and monthly GLDAS soil moisture with the wetter period over the southern and South Central Coast areas that is latter than that over the northern and North Central Coast areas. However, the spatial patterns of annual mean precipitation and soil moisture disagree, likely due to factors other than precipitation affecting the amount of moisture in the soil layers, e.g., temperature, irrigation, and drainage systems, which are inconsistent between areas. The CHIRPS Standardized Precipitation Index (SPI) is useful in capturing climate extremes, and the GLDAS Standardized Soil Moisture Index (SSI) is useful in identifying the influences of climate extremes on rice production in Vietnam. During the 2016–2018 period, there existed a reduction in the residual rice yield that was consistent with a decrease in soil moisture during the same time period.

Evaluating satellite-based evapotranspiration estimates for hydrological applications in data-scarce regions: A case in Ethiopia

Water resource development opens up opportunities for improving smallholder farmer livelihoods in sub-Saharan Africa; however, implementation of water resource interventions to ensure sustainability hinges on the availability of sufficient quantity and quality data for monitoring, analysis and planning. Such data is often acquired through instrumentation of water resources (e.g. stream flow monitoring) or the use of hydrological models. In sub-Saharan Africa, data scarcity has limited the ability to monitor and make appropriate decisions for water resource allocation and use. Data derived from remote sensing has been considered a viable option to fill this gap; however, there is limited research in the region that evaluate the quality of the remotely sensed based datasets. This study evaluated actual evapotranspiration (AET) estimates derived from Advanced Very High Resolution Radiometer (AVHRR AET) images and Moderate Resolution Imaging Spectrometer (MOD16 AET) images using estimates from a grid-based Soil and Water Assessment Tool (SWAT). The SWAT model was set up for the entire country of Ethiopia, and calibrated and validated using observed streamflow at several meso-scale watersheds in which satisfactory model performance was obtained. AET estimates from the calibrated and validated SWAT model were then used to evaluate remotely sensed based AET for three landscapes. The AVHRR AET better agreed with the SWAT-simulated AET than the MOD16 AET, although the AVHRR AET overestimated the SWAT-simulated AET in all of the landscapes. Both remotely sensed AET products showed better agreement with the SWAT-simulated AET over agriculture dominated landscapes compared to grassland and forest dominated landscapes. The findings of the study suggest that remotely sensed based AET may help to fine-tune hydrological models in agricultural landscapes in data-scarce regions to improve studies on the impacts of water management interventions aiming to ensure environmental sustainability while enhancing agricultural production, and household income and nutrition.

A Modeling Approach to Diagnose the Impacts of Global Changes on Discharge and Suspended Sediment Concentration within the Red River Basin

The Red River basin is a typical Asian river system affected by climate and anthropogenic changes. The purpose of this study is to build a tool to separate the effect of climate variability and anthropogenic influences on hydrology and suspended sediments. A modeling method combining in situ and climatic satellite data was used to analyze the discharge (Q) and suspended sediment concentration (SSC) at a daily time scale from 2000 to 2014. Scenarios of natural and actual conditions were implemented to quantify the impacts of climate variability and dams. The modeling gained satisfactory simulation results of water regime and SSC compared to the observations. Under natural conditions, the Q and SSC show decreasing tendencies, and climate variability is the main influence factor reducing the Q. Under actual conditions, SSC is mainly reduced by dams. At the outlet, annual mean Q got reduced by 13% (9% by climate and 4% by dams), and annual mean SSC got reduced to 89% (13% due to climate and 76% due to dams) of that under natural conditions. The climate tendencies are mainly explained by a decrease of 9% on precipitation and 5% on evapotranspiration, which results in a 13% decrease of available water for the whole basin.

Alteration of freshwater ecosystem services under global change - A review focusing on the Po River basin (Italy) and the Red River basin (Vietnam)

Freshwater ecosystem services are negatively affected by factors such as climate change (e.g. changes in temperature, precipitation, and sea level rise) and human interventions (e.g. agriculture practices, impoundment of dams, and land use/land cover change). Moreover, the potential synergic impacts of these factors on ecosystems are unevenly distributed, depending on geographical, climatic and socio-economic conditions. The paper aims to review the complex effects of climatic and non-climatic drivers on the supply and demand of freshwater ecosystem services. Based on the literature, we proposed a conceptual framework and a set of indicators for assessing the above-mentioned impacts due to global change, i.e. climate change and human activities. Then, we checked their applicability to the provisioning services of two well-known case studies, namely the Po River basin (Italy) and the Red River basin (Vietnam). To define the framework and the indicators, we selected the most relevant papers and reports; identified the major drivers and the most relevant services; and finally summarized the fundamental effects of these drivers on those services. We concluded that the proposed framework was applicable to the analyzed case studies, but it was not straightforward to consider all the indicators since ecosystem services were not explicitly considered as key assessment endpoints in these areas. Additionally, the supply of ecosystem services was found to draw much more attention than their demand. Finally, we highlighted the importance of defining a common and consistent terminology and classification of drivers, services, and effects to reduce mismatches among ecosystem services when conducting a risk assessment.

Evaluation and Hydrologic Validation of Three Satellite-Based Precipitation Products in the Upper Catchment of the Red River Basin, China

Satellite-based precipitation products (SPPs) provide alternative precipitation estimates that are especially useful for sparsely gauged and ungauged basins. However, high climate variability and extreme topography pose a challenge. In such regions, rigorous validation is necessary when using SPPs for hydrological applications. We evaluated the accuracy of three recent SPPs over the upper catchment of the Red River Basin, which is a mountain gorge region of southwest China that experiences a subtropical monsoon climate. The SPPs included the Tropical Rainfall Measuring Mission (TRMM) 3B42 V7 product, the Climate Prediction Center (CPC) Morphing Algorithm (CMORPH), the Bias-corrected product (CMORPH_CRT), and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) Climate Data Record (PERSIANN_CDR) products. SPPs were compared with gauge rainfall from 1998 to 2010 at multiple temporal (daily, monthly) and spatial scales (grid, basin). The TRMM 3B42 product showed the best consistency with gauge observations, followed by CMORPH_CRT, and then PERSIANN_CDR. All three SPPs performed poorly when detecting the frequency of non-rain and light rain events (<1 mm); furthermore, they tended to overestimate moderate rainfall (1–25 mm) and underestimate heavy and hard rainfall (>25 mm). GR (Génie Rural) hydrological models were used to evaluate the utility of the three SPPs for daily and monthly streamflow simulation. Under Scenario I (gauge-calibrated parameters), CMORPH_CRT presented the best consistency with observed daily (Nash–Sutcliffe efficiency coefficient, or NSE = 0.73) and monthly (NSE = 0.82) streamflow. Under Scenario II (individual-calibrated parameters), SPP-driven simulations yielded satisfactory performances (NSE >0.63 for daily, NSE >0.79 for monthly); among them, TRMM 3B42 and CMORPH_CRT performed better than PERSIANN_CDR. SPP-forced simulations underestimated high flow (18.1–28.0%) and overestimated low flow (18.9–49.4%). TRMM 3B42 and CMORPH_CRT show potential for use in hydrological applications over poorly gauged and inaccessible transboundary river basins of Southwest China, particularly for monthly time intervals suitable for water resource management.

Comparison and Bias Correction of TMPA Precipitation Products over the Lower Part of Red–Thai Binh River Basin of Vietnam

As the limitation of rainfall collection by ground measurement has been widely recognized, satellite-based rainfall estimate is a promising high-resolution alternative in both time and space. This study is aimed at exploring the capacity of the satellite-based rainfall product Tropical Rainfall Measurement Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), including 3B42V7 research data and its real-time 3B42RT data, by comparing them against data from 29 ground observation stations over the lower part of the Red–Thai Binh River Basin from March 2000 to December 2016. Various statistical metrics were applied to evaluate the TMPA products. The results showed that both 3B42V7 and 3B42RT had weak relationships with daily observations, but 3B42V7 data had strong agreement on the monthly scale compared to 3B42RT. Seasonal analysis showed that 3B42V7 and 3B42RT underestimated rainfall during the dry season and overestimated rainfall during the wet season, with high bias observed for 3B42RT. In addition, detection metrics demonstrated that TMPA products could detect rainfall events in the wet season much better than in the dry season. When rainfall intensity was analyzed, both 3B42V7 and 3B42RT overestimated the no rainfall event during the dry season but underestimated these events during the wet season. Finally, based on the moderate correlation between climatology–topography characteristics and correction factors of linear-scaling (LS) approach, a set of multiple linear models was developed to reduce the error between TMPA products and the observations. The results showed that climatology–topography-based linear-scaling approach (CTLS) significantly reduced the percentage bias (PBIAS) score and moderately improved the Nash–Sutcliffe efficiency (NSE) score. The finding of this paper gives an overview of the capacity of TMPA products in the lower part of the Red–Thai Binh River Basin regarding water resource applications and provides a simple bias correction that can be used to improve the correctness of TMPA products.

Modelling Groundwater Flow with MIKE SHE Using Conventional Climate Data and Satellite Data as Model Forcing in Haihe Plain, China

In North China Plain, accurate spatial and temporal ET and precipitation pattern is very important in the groundwater resource assessment. This study demonstrated the potential for modelling ET and groundwater processes using remote sensing data for distributed hydrological modelling with MIKE SHE codes in the Haihe Plain, China. The model was successfully validated against independent groundwater level measurements following the calibration period and the model also provided a reasonable match of the lysimeter measurements of ET. The remote sensing data included ET derived from global radiation products of Fengyun-2C geostationary meteorological satellite (FY-2C) and FY-2C precipitation products. The comparisons show that precipitation is a critical factor for the hydrological response and for the spatial distribution of ET and groundwater flow. FY-2C precipitation products has a spatial resolution of about 11 km, which thus adds more spatial variability to the most important driving variable. The ET map based on FY-2C data has a higher spatial variability than that map based on conventional data, which are caused by higher resolution of ground information. The groundwater level changes in the aquifer system are shown in the quite different spatial patterns under two models, which is affected by the significant difference between two types of precipitation. In the Haihe Plain, accurate spatial and temporal ET pattern is very important in the groundwater resource assessment that determines the recharge to the saturated zone.

Calibration of Spatially Distributed Hydrological Processes and Model Parameters in SWAT Using Remote Sensing Data and an Auto-Calibration Procedure: A Case Study in a Vietnamese River Basin

In this paper, evapotranspiration (ET) and leaf area index (LAI) were used to calibrate the SWAT model, whereas remotely sensed precipitation and other climatic parameters were used as forcing data for the 6300 km2 Day Basin, a tributary of the Red River in Vietnam. The efficacy of the Sequential Uncertainty Fitting (SUFI-2) parameter sensitivity and optimization model was tested with area specific remote sensing input parameters for every Hydrological Response Units (HRU), rather than with measurements of river flow representing a large set of HRUs, i.e., a bulk calibration. Simulated monthly ET correlations with remote sensing estimates showed an R2 = 0.71, Nash–Sutcliffe Efficiency NSE = 0.65, and Kling Gupta Efficiency KGE = 0.80 while monthly LAI showed correlations of R2 = 0.59, NSE = 0.57 and KGE = 0.83 over a five-year validation period. Accumulated modelled ET over the 5-year calibration period amounted to 5713 mm compared to 6015 mm of remotely sensed ET, yielding a difference of 302 mm (5.3%). The monthly flow at two flow measurement stations were adequately estimated (R2 = 0.78 and 0.55, NSE = 0.71 and 0.63, KGE = 0.59 and 0.75 for Phu Ly and Ninh Binh, respectively). This outcome demonstrates the capability of SWAT model to obtain spatial and accurate simulation of eco-hydrological processes, also when rivers are ungauged and the water withdrawal system is complex.