Seasonal Maize yield forecasting in South and East African Countries using hybrid Earth observation models.

Climate change still adversely affects agriculture in the sub-Saharan Africa. There is need to strengthen early action to bolster livelihoods and food security. Most governments use pre- and post-harvest field surveys to capture statistics for National Food Balance Sheets (NFBS) key in food policy and economic planning. These surveys, though accurate, are costly, time consuming, and may not offer rapid yield estimates to support governments, emergency organizations, and related stakeholders to take advanced strategic decisions in the face of climate change. To help governments in Kenya (KEN), Zambia (ZMB), and Malawi (MWI) adopt digitally advanced maize yield forecasts, we developed a hybrid model based on the Regional Hydrologic Extremes Assessment System (RHEAS) and machine learning. The framework is set-up to use weather data (precipitation, temperature, and wind), simulations from RHEAS model (soil total moisture, soil temperature, solar radiation, surface temperature, net transpiration from vegetation, net evapotranspiration, and root zone soil moisture), simulations from DSSAT (leaf area index and water stress), and MODIS vegetation indices. Random Forest (RF) machine learning model emerged as the best hybrid setup for unit maize yield forecasts per administrative boundary scoring the lowest unbiased Root Mean Square Error (RMSE) of 0.16 MT/ha, 0.18 MT/ha, and 0.20 MT/ha in Malawi's Karonga district, Kenya's Homa Bay county, and Zambia's Senanga district respectively. According to relative RMSE, RF outperformed other hybrid models attaining the lowest score in all countries (ZMB: 25.96%, MWI: 28.97%, and KEN: 27.54%) followed by support vector machines (ZMB: 26.92%, MWI: 31.14%, and KEN: 29.50%), and linear regression (ZMB: 29.44%, MWI: 31.76%, and KEN: 47.00%). Lastly, the integration of VI and RHEAS information using hybrid models improved yield prediction. This information is useful for NFBS bulletins forecasts, design and certification of maize insurance contracts, and estimation of loss and damage in the advent of climate justice.

Investigating the utility of satellite-based precipitation products for simulating extreme discharge events: an exhaustive model-driven approach for a tropical river basin in India.

Satellite-based precipitation estimates are a critical source of information for understanding and predicting hydrological processes at regional or global scales. Given the potential variability in the accuracy and reliability of these estimates, comprehensive performance assessments are essential before their application in specific hydrological contexts. In this study, six satellite-based precipitation products (SPPs), namely, CHIRPS, CMORPH, GSMaP, IMERG, MSWEP, and PERSIANN, were evaluated for their utility in hydrological modeling, specifically in simulating streamflow using the Variable Infiltration Capacity (VIC) model. The performance of the VIC model under varying flow conditions and timescales was assessed using statistical indicators, viz., R2, KGE, PBias, RMSE, and RSR. The findings of the study demonstrate the effectiveness of VIC model in simulating hydrological components and its applicability in evaluating the accuracy and reliability of SPPs. The SPPs were shown to be valuable for streamflow simulation at monthly and daily timescales, as confirmed by various performance measures. Moreover, the performance of SPPs for simulating extreme flow events (streamflow above 75%, 90%, and 95%) using the VIC model was assessed and a significant decrease in the performance was observed for high-flow events. Comparative analysis revealed the superiority of IMERG and CMORPH for streamflow simulation at daily timescale and high-flow conditions. In contrast, the performances of CHIRPS and PERSIANN were found to be poor. This study highlights the importance of thoroughly assessing the SPPs in modeling diverse flow conditions.

Projected hydrologic changes over the north of the Iberian Peninsula using a Euro-CORDEX multi-model ensemble.

This study explores the impacts of climate change on the hydrology of the headwater areas of the Duero River Basin, the largest basin of the Iberian Peninsula. To this end, an ensemble of 18 Euro-CORDEX model experiments was gathered for two periods, 1975-2005 and 2021-2100, under two Representative Concentration Pathways (RCP4.5 and RCP8.5), and were used as the meteorological forcings of the Variable Infiltration Capacity (VIC) during the hydrological modelling exercise. The projected hydrologic changes for the future period were analyzed at annual and seasonal scales using several evaluation metrics, such as the delta changes of the atmospheric and land variables, the runoff and evapotranspiration ratios of the overall water balance, the snowmelt contribution to the total streamflow and the centroid position for the daily hydrograph of the average hydrologic year. Annual streamflow reductions of up to 40% were attained in various parts of the basin for the period 2071-2100 under the RCP8.5 scenario, and resulted from the precipitation decreases in the southern subwatersheds and the combined effect of the precipitation decreases and evapotranspiration increases in the north. The runoff and the evapotranspiration ratios evinced a tendency towards an evaporative regime in the north part of the basin and a strengthening of the evaporative response in the south. Seasonal streamflow changes were mostly negative and dependent on the season considered, with greater detriments in spring and summer, and less intense ones in autumn and winter. The snowmelt contribution to the total streamflow was strongly diminished with decreases reaching -80% in autumn and spring, thus pointing to a change in the snow regime for the Duero mountains. Finally, the annual and seasonal changes of the centroid position accounted for the shape changes of the hydrograph, constituting a measure of seasonality and reflecting high correlations degrees with the streamflow delta changes.

Runoff sensitivity of Indian sub-continental river basins.

Understanding the sensitivity of water availability in the current and future climate in the Indian sub-continent is vital for food and water security. Using the Variable Infiltration Capacity (VIC) model and Budyko's framework with two observational datasets, we estimated water budget and mean annual runoff sensitivity to precipitation and potential evapotranspiration (PET) over 18 major river basins and 222 sub-basins in the Indian sub-continent. The river basins located in the north experienced a decline in mean annual precipitation while the basins in the south witnessed an increase in mean annual precipitation. Declined precipitation and increased PET resulted in a decrease in mean annual runoff in Brahmaputra, Ganga, and Indus basins during 1980-2014. On the other hand, mean annual runoff has increased in Sabarmati, South Coast, Subernarekha, Tapi, Mahanadi, East coast, Cauvery, and Brahmani river basins. Mean annual AET estimated using the Budyko's framework was underestimated while mean annual total runoff was overestimated for the majority of the basins in comparison to the estimates from the VIC model. Moreover, the Budyko's framework with both observational datasets underestimated runoff sensitivity to the changes in precipitation and PET in comparison to the VIC model. Runoff is more sensitive to change in precipitation than PET for the majority of the river basins highlighting the importance of changes in precipitation for water availability in the Indian sub-continent. The VIC model simulated runoff and evapotranspiration are in better agreement with the observations in comparison to the estimates from the Budyko's framework. However, a large uncertainty was found in water budget and runoff sensitivity estimated using the VIC and Budyko's models, which highlights the importance of considering multiple models for estimation of the water budget and runoff sensitivity in the sub-continental river basins.

Predicting the spatiotemporal characteristics of flash droughts with downscaled CMIP5 models in the Jinghe River basin of China.

Climate warming greatly affects the frequency and intensity of flash droughts, which can cause huge damage to agriculture. It is important to understand the changing rules of future flash droughts and take precautionary measures in advance. Thus, we focused on the flash drought characteristic of the Jinghe River basin using variable infiltration capacity (VIC) model and four-model ensemble in the two representative concentration pathway scenarios. Four-model ensemble mean can well capture hydrological changes in the reference period. The heat wave flash drought (HWFD) and the precipitation deficit flash drought (PDFD) mainly occur in the northern during reference period. The HWFD and PDFD have shown a linear growth trend in the future and both shown higher growth rates in the RCP8.5 scenario. The frequency of occurrence (FOC) increments of flash droughts were relatively high in the southern Jinghe River basin. And the HWFD and the PDFD mainly occurred in May-September. Further results indicate that the contribution of the maximum temperature to HWFD was the biggest (greater than 0.7), followed by evapotranspiration (ET) and soil moisture (SM). The contribution of maximum temperature to PDFD was the biggest (greater than 0.5), followed by precipitation and ET. Global warming in the twenty-first century is likely to lead to intensification of flash droughts. Therefore, measures and suggestions were proposed to effectively respond to flash droughts in our study.

Effects of irrigation on water and energy balances in the Heihe River basin using VIC model under different irrigation scenarios.

Investigations of the water and energy balance in large river basins is one of the most important and contemporary issues, which is helpful to guide agricultural production and regional water resource management. Traditionally, water and energy balance have been assessed by field-scale experiments. However, it is not easy to find the effective ways for a whole region using limited observed data from on-farm experiments. In our study, the effects of irrigation water on surface water and energy balance fluxes are examined by employing the Variable Infiltration Capacity (VIC) model and irrigation scheme, for the upper and middle reaches of the Heihe River Basin in Northwest China. The model simulations are calibrated and validated using both streamflow records at a gauge station and eddy covariance observations at two stations. Besides, three irrigation scenarios are set as full irrigation, 90% and 75% of irrigation water requirement (IWR). The results showed the infiltration curve parameter (b) and the thickness of lower soil moisture layer (d2) are the most sensitive model parameters. Long-term irrigation activities lead to a greater evapotranspiration (or latent heat). With considering local irrigation water-using coefficient for the period 2001-2010 of 0.527, the total IWR is about 2.81 × 109 m3/year (the net IWR is about 1.48 × 109 m3/year). Compared with the no-irrigation baseline, the increase in latent heat flux (about 4.45 W/m2) or the significant decrease in Bowen Ratio (about 1.05) due to full irrigation activities is accompanied by a decrease in annual average surface temperature (about 0.076 °C) for the middle reaches of the Heihe River basin during the 10-year period.

Hydrological model using ground- and satellite-based data for river flow simulation towards supporting water resource management in the Red River Basin, Vietnam.

The Red River basin (RRB) exhibits substantial variation of water resource seasonally and annually. Sustainable water resource management in the RRB has been challenging due to the lack of in situ hydrological measurement data over the basin-wide scale. To address this issue, this study aimed to perform the setting up, calibration, and validation of the variable infiltration capacity (VIC) hydrological model forced with ground- and satellite-based datasets at a high spatial resolution of 0.1° for simulating the daily river flow of the Red River system in the RRB during the period of 2005-2014. By using the finely resolved land cover characterization with 15 types of land cover and leaf area index - the most important feature of vegetation that significantly influences the simulation of hydrological variables provided by the spatially distributed satellite remote sensing data, this study would not only address the poor data availability over the RRB but also enhance the accuracy of model simulation. The simulation results generally indicated that the calibrated VIC model could satisfactorily capture the river flow dynamics of the Red River system in the RRB. The VIC model's underestimated river flow compared to the observed data during the dry season for the downstream stations was likely due to the operation of the large man-made reservoirs and dams in the upstream catchments of the RRB that not represented by the VIC model. The findings also suggested that for further improving the VIC model performance, the use of more spatially representative meteorological data provided by satellite remote sensing should be considered in future studies.

A new method and a new index for identifying socioeconomic drought events under climate change: A case study of the East River basin in China.

Drought is a complex natural hazard that may have destructive damages on societal properties and even lives. Generally, socioeconomic drought occurs when water resources systems cannot meet water demand, mainly due to a weather-related shortfall in water supply. This study aims to propose a new method, a heuristic method, and a new index, the socioeconomic drought index (SEDI), for identifying and evaluating socioeconomic drought events on different severity levels (i.e., slight, moderate, severe, and extreme) in the context of climate change. First, the minimum in-stream water requirement (MWR) is determined through synthetically evaluating the requirements of water quality, ecology, navigation, and water supply. Second, according to the monthly water deficit calculated as the monthly streamflow data minus the MWR, the drought month can be identified. Third, according to the cumulative water deficit calculated from the monthly water deficit, drought duration (i.e., the number of continuous drought months) and water shortage (i.e., the largest cumulative water deficit during the drought period) can be detected. Fourth, the SEDI value of each socioeconomic drought event can be calculated through integrating the impacts of water shortage and drought duration. To evaluate the applicability of the new method and new index, this study examines the drought events in the East River basin in South China, and the impact of a multi-year reservoir (i.e., the Xinfengjiang Reservoir) in this basin on drought analysis is also investigated. The historical and future streamflow of this basin is simulated using a hydrologic model, Variable Infiltration Capacity (VIC) model. For historical and future drought analysis, the proposed new method and index are feasible to identify socioeconomic drought events. The results show that a number of socioeconomic drought events (including some extreme ones) may occur in future, and the appropriate reservoir operation can significantly ease such situation.