Study region: Irrawaddy River, Myanmar, Southeast Asia Study focus: Detection of streamflow changes serves as a critical foundation for water resources management. The data-scarce Irrawaddy is ideally positioned for this study, as investigations of regional hydrology are lacking, despite being one of the largest rivers in Southeast Asia. Hence, this work achieves an unprecedented and detailed analysis of the Irrawaddy streamflow variability from 2001 to 2023 due to climate, dams, and land cover change. The assessment integrated trend analyses with diagnostic modeling using the Variable Infiltration Capacity with reservoir module (VIC-Res) for two sets of scenarios: a set with and without dam operations, and another without dams using fixed 2001 and 2023 land cover. New hydrological insights for the region: Streamflow significantly decreased in the lower Irrawaddy. Moreover, the Irrawaddy streamflow variability was mainly attributed to climate for two proven reasons. First, both land cover change and dam operations contributed marginally to mean seasonal and annual streamflow changes (around +/- 2 %) due to small land cover change basin-wide (within +/- 5 %) and that all dams are on low-order tributaries with the degree of regulation of only 2 %. Compared to the effect of land cover change on streamflow, dams exerted higher flow changes in magnitude and variability. Second, precipitation and streamflow maintained a statistically significant linear correlation (p < 0.01) and exhibited similar decreasing tendency, despite no significance detected for precipitation. This study enhances understanding of the Irrawaddy's contemporary hydrology to support basin-scale water resources management.

To provide a better subseasonal-to-seasonal (S2S) hydrological forecast, it is essential to investigate the factors that control streamflow prediction at time scales beyond that of traditional weather forecasts. Using a hydrological forecast framework built around NASA's Catchment-CN land model and GEOS S2S forecast meteorology, this study examines the predictive skill of subseasonal (similar to 30 days) streamflow in Southeast Asia and shows how that skill may be improved in combination with satellite-based rainfall information in areas for which the rain-gauge measurements are particularly poor. Initialized at four different times of a year, the prediction skill along the Irrawaddy River in Myanmar was significantly improved, going from no skill up to a correlation coefficient R of 0.65 during the wet season and up to 0.55 during the following transitional period by introducing Integrated Multi-satellitE Retrievals for GPM (IMERG) satellite-based precipitation into our land initialization methodology. The streamflow forecast skill along the Mekong River was reasonably high (R of 0.6-0.7) during the dry season before and after the utilization of IMERG data, and the wet-season forecast skill modestly increased up to R of 0.8. The accurate land initialization is found to contribute dominantly to the predictive skill of subseasonal streamflow; however, low rainfall forecast skill occasionally offsets the positive contribution from the land initialization. Our findings suggest an alternative way to enhance S2S hydrological forecasting in other large river basins where rain gauge information is limited and illustrate the need for a careful application of forecast rainfall to hydrological prediction during the transitional seasons.

Rice production in Myanmar is severely affected by the uncertain weather changes and political unrest especially due to the conflicts of coup after February 2021. Although recent improvements in satellite remote sensing have helped national to field-scale yield estimation and forecasting, the challenges through quality of reference data questions the quality of forecasts and estimation. In Myanmar, agricultural production data is publicly available at regional scale (equivalent to province level in USA) from 2012 to 2020. The consistent, timely and granular information on yield estimates at lower to higher admin unit level is crucial need to support sustainability of livelihood. In support to these efforts to improve food insecurity analysis and climate resilient livelihood formation, SERVIR - a joint USAID and NASA initiative - has implemented rice yield model using machine learning and satellite data to guide the decision makers in the region. This study used time-series (2012-2023) information on satellite derived vegetation indices, weather variability, topography and water use to predict the rice yield at regional to field scale using artificial neural network, random forest and boosted decision tree based artificial intelligence (AI) modeling. The model was tested spatially as well as temporally and overall average R2 observed varied from 0.6 to 0.7. The presented rice yield model in this study provides carefully evaluated rice yield estimations at regional to field-scale across Myanmar rice-growing region. The consistent rice estimates from this study will help decision makers in the region. This study is the first to model rice yield at this high-resolution with locally tuned information. The model and algorithm developed in this study have the potential of operational use for further needs.

Estimation of accurate soil physical and hydraulic properties are of prime importance for the management of water resources in agriculture-dominant regions. This study introduces a simplified framework for estimating soil physical and hydraulic properties crucial for managing agricultural water resources. The developed framework optimizes soil properties for the Regional Hydrological Extremes Assessment System (RHEAS) to enhance the performance of its core hydrological model, Variable Infiltration Capacity (VIC). These soil properties were optimized using six years (2015-2021) of satellite soil moisture observations from NASA's Soil Moisture Active Passive (SMAP) mission with a modified Shuffled Complex Evolution (SCE-UA) optimization algorithm. A total of three most sensitive soil properties that control model soil moisture simulations, such as Ksat (Saturated hydraulic conductivity), expt (exponent parameter in Campbell's equation for hydraulic conductivity), and bd (Bulk density) were optimized for the Lower Mekong River (LMR) basin. To better assess the impact of optimized soil properties, streamflow simulation as well as agricultural drought severity assessment, were estimated using the RHEAS framework's VIC Routing module and Soil Moisture Deficit Index (SMDI) module, respectively. The streamflow simulation involved four approaches: an initial open-loop setup, one optimized with SMAP soil moisture data (SMAP), another optimized with actual streamflow data (Runoff), and a final one combining the previous two datasets (SMAP_Runoff). Switching from the initial setup to the SMAP-optimized model increased the Nash-Sutcliffe Efficiency (NSE) by 56.4 % and upgrading from the streamflow-optimized to the combined data model raised the NSE by 21.9 %. This showcases the benefits of optimizing soil properties for more accurate simulations. Furthermore, the optimized model accurately represented the severity and extent of historical agricultural droughts, aligning with regional drought reports of LMR basin. This framework offers a valuable tool for hydrological modeling and drought management, particularly in data-scarce and agriculture-intensive regions, informing agricultural water resource management, irrigation decision-making, and food security initiatives within the LMR basin and beyond.

In the absence of in-situ data, satellite radar altimetry, critical for studies requiring water elevation data, faces challenges in outlier removal over reservoirs, influenced by surrounding land. We present JASTER (Jason Altimetry Stand-Alone Tool for Enhanced Research), an open-source, fully automated tool processing Jason-2/3 altimetry data for water elevation time series generation over a user-defined inland water body. JASTER uses two outlier removal approaches. The first method employs interquartile range (IQR)-based filtering and K-means clustering. The second method incorporates water occurrence (WO) and Digital Elevation Model (DEM)-derived elevation thresholds. The Hampel filter embedded in JASTER further removes non-physical peaks in the time series caused by signal noise. We validated JASTER’s capabilities using 44 Jason altimeter crossings over 37 water bodies in the US and Canada. We assessed the significance of applying water occurrence and elevation thresholds, and investigated how the Hampel filter parameters and satellite crossing lengths affect accuracy. In all case studies, the Hampel filter significantly increased the consistency between JASTER-derived water elevations and in-situ data. Moreover, the DEM + WO-based method outperformed the IQR-based outlier removal approach in some cases where the greater number of altimeter footprints belonged to land instead of water.

AbstractNepal’s hilly and mountainous regions are highly susceptible to landslides triggered by extreme precipitations. The prevalence of such landslides has increased due to climate change-induced extreme hydro-meteorological conditions. These recurring landslides have significantly impacted the road transport infrastructure, which is the economic lifeline for cities and socio-economic mobility of rural communities in the hilly and mountainous regions of the country. This study modelled extreme rainfall scenarios for the current 1976–2005 baseline and future horizons of 2030, 2050, and 2080 to develop high-resolution 1 km × 1 km mean precipitation datasets under RCP4.5 and RCP8.5. Based on these extreme precipitation scenarios, we developed high-resolution landslide hazard models adopting integrated weighted index by combining the Frequency Ratio (FR) and Analytical Hierarchical Process (AHP) methods using multi-variate factors. The multi-variate factors included three terrain parameters—slope, aspect, and elevation; two soil parameters—lithology and soil type; two Euclidean distance parameters from the likely sources—distance from the lineaments and distance from the stream/river; an anthropogenic parameter—land use; and the climate parameter—the mean annual rainfall for four-time horizons and two RCPs. These parameters were spatially modelled and combined using the weighted overlay method to generate a landslide hazard model. As demonstration case studies, the landslide hazard models were developed for Bagmati and Madhesh provinces. The models were validated using the Receiver Operating Characteristic curve (ROC) approach, which showed a satisfactory 81–86% accuracy in the study area. Spatial exposure analysis of the road network assets under the Strategic Road Network (SRN) was completed for seven landslide hazard scenarios. In both Bagmati and Madhesh provinces, the exposure analysis showed that the proportion of road sections exposed to landslide hazard significantly increases for the future climate change scenarios compared to the current baseline scenario.

Crop yield predictions on inter-seasonal to inter-annual horizons are subject to a diverse set of uncertainties associated with climate forecast scenarios. However, the uncertainties associated with climate-related parameters can be marginally controlled (reduced) over the growing season, i.e., from planting through harvest, leveraging a suite of climate forecast ensembles as forcings. In this study, we present a novel approach that combines a coupled hydrologic-crop modeling framework with probabilistic forecasts to characterize and reduce uncertainties in seasonal rice yields predictions. By weighting real-time (nowcast) and forecast climate forcings, the comprehensive framework accurately quantifies uncertainties associated with climate forecasts. At a provincial scale, the crop model extensively captured the uncertainties in yields whilst significantly complementing observations over the growing season. We observed that the spread of yield predictions gradually decreased over time (i.e., towards harvest) as subsequent timeframes incorporated a higher degree of present conditions/forcings and reduced reliance on forecasts. Furthermore, we investigated the information exchange between yields and hydrologic/drought variables over different time frames within the season. Notably, we found a higher synchronization of information transfer between yields, dryspells, and minimum air temperatures towards the end of season, indicating strong explicit links between these variables and crop yields. These outcomes have significant implications on crop yield forecasting and nowcasting, particularly in data-poor regions. By providing a better understanding of the uncertainties associated with seasonal climate forecasts and the interplay between hydrologic variables, drought conditions, and crop yields, this research can aid in improving decision-making processes related to agricultural planning, and risk management. Moreover, these insights can inform assessments of economic, social, and environmental impacts of drought in agricultural systems.

Abstract The goal in this commentary is to share the development of the NASA Applied Science pre‐launch protocol called the Early Adopter Program (EAP) that is designed to build user‐readiness of planned satellite Earth observing missions proactively and before the launch. Here we focus in particular on the Surface Water and Ocean Topography satellite mission EAP as an illustration of benefits of such a program of proactive and sustained user community engagement. Such a commentary will be of value to other satellite Earth observation missions that are currently in service, scheduled for launch or prioritized for development in the near future.

The Lower Mekong region is particularly prone to natural hazards caused by extreme rainfall. During monsoon seasons from June to October, heavy rainfall triggers severe flash floods and landslides, posing a threat to human lives and livelihoods. Global forecast products struggle to provide reliable estimates of extreme rainfall at regional scale, which is a big challenge in their integration in early warning systems. A newly released version of Climate Hazards Center InfraRed Precipitation with Stations (CHIRPS) Global Ensemble Forecast System (GEFS) dataset is a bias-corrected and downscaled product derived from National Centers for Environmental Prediction (NCEP)-GEFS. CHIRPS-GEFS product provides up to 16 days of rainfall forecasts at 5km/daily spatio/temporal resolution. This study evaluates the spatial and temporal performance of the CHIRPS-GEFS for extreme precipitation in the Lower Mekong region during monsoon seasons from 2014 to 2019. Rainfall forecasts from 1 to 5-days lead-time are analyzed against the bias-corrected Integrated Multi-satellitE Retrievals for GPM (IMERG) over the lower Mekong region. The performance is assessed using both categorical and continuous statistics such as the probability of detection, false alarm ratio, critical success index, correlation coefficient, and root mean squared error. Results describe the spatial and temporal strengths and limitations of the CHIRPS-GEFS and the influence of geomorphological conditions on its performance. This analysis provides valuable information on CHIRPS-GEFS possible integration in the lower Mekong landslide forecasting model for region-based landslide hazard assessment and situational awareness (LHASA) along the lines of the global LHASA framework.

Planting date (PD) is one of the critical component in crop modeling that has a large-scale impact on seasonal yield estimation. Here, we aimed to estimate the various paddy/rice attributes (particularly, acreage and PD) using the Sentinel-1A observations, and assessing the impact of such key parameters on end-of-season yields. The method comprises: (1) a multi-temporal time-series analysis algorithm of Sentinel-1A observations for rice mapping, (2) a PD retrieval algorithm, and (3) rice yield estimation using the Synthetic Aperture Radar (SAR)-derived PD in a modeling system. Our study was implemented over the rainfed paddy fields in Cambodia between 2017 thru 2020. We demonstrated that the backscatter ratio (σVH0σVV0) from Sentinel-1A can improve the accuracy of rice mapping, while σVH0 backscatter can provide PD estimations. Our results showcased reasonable/good performance in mapping the paddy fields and estimating PD in the study area. PD found in March and April were indicative of irrigation application in some parts of Cambodia, specifically in the southeastern part (also part of the Mekong delta) where irrigation in paddy fields is a common practice. Differences between the crop-calendar based PD (June 1) and the SAR-derived PD can range as much as 75 days. In the rice-dominated provinces such as Banteay-Meanchey and Svay-Rieng of Cambodia, when SAR-derived PD are used, rice yield normalized bias improved compared to the fixed PD data by a mean of 7–12 % and 30–48 % respectively within the study period. In addition, the PD derived from SAR also significantly reduced the uncertainty in yield estimation. Overall, this study demonstrated the potential of using the SAR-derived PD in improving the rice yield estimation over rainfed paddy fields.

Recurring drought in the Lower Mekong countries has inflicted enormous pressure on the natural ecosystem, rice productivity, and water resources. A regional scale assessment over Cambodia was carried out to examine the linkages between rice productivity and meteorological/hydrologic drought variability from 2000 to 2016. We implemented a comprehensive drought and crop yield information system, the Regional Hydrologic Extremes Assessment System (RHEAS) framework, that couples a hydrologic model with a crop growth model to capture the subtle, intrinsic nature of drought, and assess the impact on inter-seasonal and intra-annual rice yields. Simulations based on RHEAS show good agreement with observations (R-2 similar to 0.65 for soil moisture from the hydrologic model; R-2 similar to 0.84 for crop model). Using a suite of standardized drought indices, the onset and prevalence of dry and wet periods throughout the study period were examined at multiple temporal scales. The temporal variability in drought intensity exhibited higher water stress during the initial months (Mar-May), indicating prevalence of medium to severe dry conditions prior to the planting season. However, the onset of monsoon at the beginning of the growing season (June) resulted in the prevalence of normal to moderate wet conditions. A linear trend analysis for the period 2000-2016 showed a consistent increase (similar to 2900 kg/ha in 2000 to similar to 3550 kg/ha in 2016) in rice yields, although drought-stricken provinces showed lower yields (similar to 1650 kg/ha) throughout the study period. Overall, a continuous increase in annual rice yields irrespective of the stress conditions was noted with no clear pattern linking drought parameters with crop yields on a regional scale. The application of chemical-based fertilizers has steadily increased over the years since 2008 and the consistent increase in observed rice yields correlated with increased fertilizer use (R-2 similar to 0.84). Information from the hydrologic and crop model components within RHEAS enables development of critical regional and local thresholds, reflecting the increasing levels of risk and vulnerability towards drought.

Agriculture production largely depends on weather conditions and is extremely prone to natural hazards. A more frequent and severe occurrence of natural hazards such as storms and floods has put food security at increased risk in recent decades. Evaluating the true impact (loss and damage) of disaster in the agriculture sector is very challenging. The present study focusses on using a zrandomized field experimental approach at both district and micro agricultural-plot levels to investigate the impact of floods on agricultural yields in Sri Lanka and its effect on farmers who are averse to taking risks and those who are willing to take risks. A detailed site selection technique has been used in the study. The dissimilarity in difference estimates indicates that flood-affected households have experienced the loss of paddy and non-paddy crops. However, the net loss of non-paddy is higher than that in paddy. Farmers offset this loss by expanding crop cultivated areas zthat utilize soaked fields after the flood, though there are risks of pest attack and diseases. The results are not driven by household-specific characteristics and are robust to several specifications, different crop types and alternative flood-severity measures.

In this study, a method was developed for the baseline characterization of river bathymetry and time-varying heights using globally available datasets from the Shuttle Radar Topography Mission (SRTM) elevation data and Landsat visible imagery. Using independent data on river water elevations from satellite altimetry, the SRTM-Landsat approach was verified as to how well it can work for baseline characterization. The technique was demonstrated for Chindwin River locations in Myanmar that were also independently sampled by Sentinel 3A and Jason 3 altimeters. The Modified Normalized Difference Water Index (MNDWI) was used for estimating the water areas and widths using Landsat 8 from 2016 to 2019. A comparison of SRTM-Landsat with Sentinel 3A/Jason 3-based elevation changes resulted in a correlation coefficient up to 0.89 and 0.82 using area-elevation and width-elevation curves, respectively. The presence of river islands during the dry season resulted in a weaker correlation between our proposed SRTM-Landsat technique and altimeter water elevations. This case study over the Chindwin River in Myanmar demonstrated that the use of the SRTM-Landsat combined technique could yield an acceptable baseline for characterization of river bathymetry and time-varying heights at ungauged locations around the world.

Climate change has a considerable impact on weather patterns worldwide. Therefore, planning and decision-making processes based on information on farmers' traditional and indigenous knowledge may no longer be accurate and useful. Respective national and local authorities have not been giving due attention to address this issue, and farmers and their dependents have been facing difficulties to sustain their livelihoods in the face of climate change. To ensure enhanced agro-ecosystem services and functions as part of policy interventions at national and sub-national levels, a 4-day training course was developed and conducted for practitioners and policymakers on "Agro-ecosystem Resilience in a Changing Climate". The training course created a pool of master trainers from government, academia, and non-governmental organizations (NGOs) in understanding agro-ecosystems, their functions and threats posed to them by weather and climate change in order to build resilience. The target countries were Nepal, Thailand and Sri Lanka. An evaluation questionnaire was developed (Likert scale and short answer types) to analyze participants' feedback and lessons learnt from the training courses. The results were mostly constructive and, in most cases, positive. The attendees gained sufficient knowledge to implement the adaptive measures as well as it opened new avenues of collaboration for the stakeholders.

The Tonle Sap Lake (TSL) is the largest natural freshwater lake in Southeast Asia and is called the "heart of the lower Mekong" due to its high aquatic biodiversity and is considered as one of the most productive freshwater ecosystems of the world. Its floodplain eco-system, which is strongly tied to seasonal flood pulse, is extremely important for food security, trade and economy of Cambodia, supporting the livelihoods of about 1.7 million people. On the other hand, flood can also be extremely devastating in the region along the TSL. In recent years, studies have pointed out that rapid growing number of water infrastructures as well as future climate changes may alter the hydrological cycle of the Mekong River Basin (MRB) and are expected to influence the flood pulse of the TSL and surrounding TSL floodplain. Therefore, it is timely to understand historical inundation extent and predict its likely future state. In this study, we proposed a Rotated Empirical Orthogonal Function (REOF) analysis-based daily inundation extent estimation framework, integrating multi-temporal stack of Sentinel-1A Synthetic Aperture Radar (SAR) imagery and Jason-series satellite altimetry data. The framework can generate daily, cloud-free and gap-free inundation extents for any given time depending on the altimetry data provided. A long-term El Nino and Southern Oscillation (ENSO) index-based daily TSL level forecasting method with months of lead time was also proposed to fulfill the framework's forecasting capacity. In this study, the framework was adopted in the TSL floodplain area for hindcast (2003 to 2015) and forecast (January to July 2019) of daily inundation extents. Estimated inundation extents were cross-compared with MODIS-derived and Sentinel-1-derived inundation maps, resulting in up to higher than 90% of Critical Success Index (CSI). The proposed framework has (1) innovative capacity of estimation of future daily areal inundation extents and is (2) a fully remote sensing-based framework which can empower local authorities tasked with water resource management decisions without relying on upstream countries. The framework has potential to be implemented in other major river basins or wetlands (e.g., Amazon River Basin, and Congo River Basin). The implementation on SAR imagery from other satellites with different bands of electromagnetic wave is also possible but requires more investigation.