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Reliable weather forecasts are valuable in a number of applications, such as agriculture, hydropower, and weather-related disease outbreaks. Global weather forecasts are widely used, but detailed evaluation over specific regions is paramount for users and operational centers to enhance the usability of forecasts and improve their accuracy. This study presents evaluation of the Global Forecast System (GFS) medium-range (1–15 day) precipitation forecasts in the nine subbasins of the Nile basin using NASA’s Integrated Multisatellite Retrievals (IMERG) Final Run satellite–gauge merged rainfall observations. The GFS products are available at a temporal resolution of 3–6 h and a spatial resolution of 0.25°, and the version-15 products are available since 12 June 2019. GFS forecasts are evaluated at a temporal scale of 1–15 days, a spatial scale from 0.25° to all the way to the subbasin scale, and for a period of one year (15 June 2019–15 June 2020). The results show that performance of the 1-day lead daily basin-averaged GFS forecast performance, as measured through the modified Kling–Gupta efficiency (KGE), is poor (0 < KGE < 0.5) for most of the subbasins. The factors contributing to the low performance are 1) large overestimation bias in watersheds located in wet climate regimes in the northern hemispheres (Millennium watershed, Upper Atbara and Setit watershed, and Khashm El Gibra watershed), and 2) lower ability in capturing the temporal dynamics of watershed-averaged rainfall that have smaller watershed areas (Roseires at 14 110 km2 and Sennar at 13 895 km2). GFS has better bias for watersheds located in the dry parts of the Northern Hemisphere or wet parts of the Southern Hemisphere, and better ability in capturing the temporal dynamics of watershed-average rainfall for large watershed areas. IMERG Early has better bias than GFS forecast for the Millennium watershed but still comparable and worse bias for the Upper Atbara and Setit and Khashm El Gibra watersheds. The variation in the performance of the IMERG Early could be partly explained by the number of rain gauges used in the reference IMERG Final product, as 16 rain gauges were used for the Millennium watershed but only one rain gauge over each Upper Atbara and Setit and Khashm El Gibra watershed. A simple climatological bias correction of IMERG Early reduces in the bias in IMERG Early over most watersheds, but not all watersheds. We recommend exploring methods to increase the performance of GFS forecasts, including postprocessing techniques through the use of both near-real-time and research-version satellite rainfall products.

Mountain snowpacks are transitioning to experience less snowfall and more rainfall as the climate warms, creating more persistent low- to no-snow conditions. This precipitation shift also invites more high-impact rain-on-snow (ROS) events, which have historically yielded many of the largest and most damaging floods in the western United States. One such sequence of events preceded the evacuation of 188,000 residents below the already-damaged Oroville Dam spillway in February 2017 in California’s Sierra Nevada. Prior studies have suggested that snowmelt during ROS dramatically amplified reservoir inflows. However, we present evidence that snowmelt may have played a smaller role than previously documented (augmenting terrestrial water inputs by 21%). A series of hydrologic model experiments and subdaily snow, soil, streamflow, and hydrometeorological measurements demonstrate that direct, “passive” routing of rainfall through snow, and increasingly efficient runoff driven by gradually wetter soils can alternatively explain the extreme runoff totals. Our analysis reveals a crucial link between frequent winter storms and a basin’s hydrologic response—emphasizing the role of soil moisture “memory” of within-season storms in priming impactful flood responses. Given the breadth in plausible ROS flood mechanisms, this case study underscores a need for more detailed measurements of soil moisture along with in-storm changes to snowpack structure, extent, energy balance, and precipitation phase to address ROS knowledge gaps associated with current observational limits. Sharpening our conceptual understanding of basin-scale ROS better equips water managers moving forward to appropriately classify threat levels, which are projected to increase throughout the mid-21st century.

Medium-range (1–15 day) precipitation forecasts are increasingly available from global weather models. This study presents evaluation of the Global Forecast System (GFS) for the Volta river basin in West Africa. The evaluation was performed using two satellite-gauge merged products: NASA’s Integrated Multi-satellitE Retrievals (IMERG) “Final Run” satellite-gauge merged rainfall observations, and the University of California Santa Barbara’s Climate Hazard’s group Infrared Precipitation with Stations (CHIRPS). The performance of GFS depends on the climate zone, with underestimation bias in the dry Sahel climate, overestimation bias in the wet Guinea Coastal climate, and relatively no bias in the moderately wet Savannah climate. Averaging rainfall over the watershed of the Akosombo dam (i.e., averaging across all three climate zones), the GFS forecast indicates low skill (Kling-Gupta Efficiency KGE = 0.42 to 0.48) for the daily, 1-day, lead GFS forecast, which deteriorates further as the lead time increases. A sharp decrease in KGE occurred between 6 to 10 days. Aggregating the forecasts over long timescales improves the accuracy of the GFS forecasts. On a 15-day accumulation timescale, GFS shows higher skills (KGE = 0.74 to 0.88).

Weather forecast information has the potential to improve water resources management, energy, and agriculture. This study evaluates the accuracy of medium-range (1–15 day) precipitation forecasts from the Global Forecast System (GFS) over watersheds of eight major dams in the Niger river basin. The Niger basin lies in three latitudinal/climatic sub-regions: Sahel (latitude > 12° N) with annual rainfall of rainfall 400–600 mm, Savannah (latitude 8°–12° N) with annual rainfall of 900–1200 mm, and Guinea Coast (latitude 4°–8° N) with annual rainfall of 1500–2000 mm. The GFS forecast tends to overestimate rainfall in the Guinea Coast and western parts of the Savannah, but estimates well in the Sahel. The overall performance of daily GFS forecast was found to be satisfactory for two watersheds, namely, Kainji (the largest watershed in the basin, predominantly located in the Sahel), and Markala (the second largest watershed, located partly in the Sahel and partly in the Savannah). However, the performance of daily GFS forecast was found to be unsatisfactory in the remaining six watersheds, with GFS forecasts characterized by large random errors, high false alarm, high overestimation bias of low rain rates, and large underestimation bias of heavy rain rates. The GFS forecast accuracy decreases with increasing lead time. The accuracy of GFS forecasts could be improved by applying post-processing techniques involving near-real time satellite rainfall products.

Accurate rainfall forecasts on timescales ranging from a few hours to several weeks are needed for many hydrological applications. This study examines bias, skill and reliability of four ensemble forecast systems (from Canada, UK, Europe, and the United States) and a multi‐model ensemble as applied to Ethiopian catchments. By verifying these forecasts on hydrological catchments, we focus on spatial scales that are relevant to many actual water forecasting applications, such as flood forecasting and reservoir optimization. By most verification metrics tested, the bias corrected European model is the best individual model at predicting daily rainfall variations, while the Canadian model shows the most realistic ensemble spread and thus the most reliable forecast probabilities, including those of extreme events. The skill of the multi‐model ensemble outperforms individual models by most metrics, and is skillful up to 9 days ahead. Skill is higher for the 0–5 day model accumulation than for the first 24 h, suggesting that timing errors strongly penalize the skill of forecasts with shorter accumulation periods. Due to seasonality in the model biases, bias correction is best applied to each month individually. Forecasting extreme rainfall is a challenge for Ethiopia, especially over mountainous regions where positive skill is only reached after bias correction. Compared to individual models, the multi‐model ensemble has a higher probability of detecting extreme rainfall and a lower false alarm rate, with usable skill at 24 h lead times.

Earth observations offer potential pathways for accurately closing the water and energy balance of watersheds, a fundamental challenge in hydrology. However, previous attempts based on purely satellite‐based estimates have focused on closing the water and energy balances separately. They are hindered by the lack of estimates of key components, such as runoff. Here, we posit a novel approach based on Budyko’s water and energy balance constraints. The approach is applied to quantify the degree of long‐term closure at the watershed scale, as well as its associated uncertainties, using an ensemble of global satellite data sets. We find large spatial variability across aridity, elevation, and other environmental gradients. Specifically, we find a positive correlation between elevation and closure uncertainty, as derived from the Budyko approach. In mountainous watersheds the uncertainty in closure is 3.9 ± 0.7 (dimensionless). Our results show that uncertainties in terrestrial evaporation contribute twice as much as precipitation uncertainties to errors in the closure of water and energy balance. Moreover, our results highlight the need for improving satellite‐based precipitation and evaporation data in humid temperate forests, where the closure error in the Budyko space is as high as 1.1 ± 0.3, compared to only 0.2 ± 0.03 in tropical forests. Comparing the results with land surface model‐based data sets driven by in situ precipitation, we find that Earth observation‐based data sets perform better in regions where precipitation gauges are sparse. These findings have implications for improving the understanding of global hydrology and regional water management and can guide the development of satellite remote sensing‐based data sets and Earth system models., Key Points A Budyko‐based approach to water and energy balance closure mitigates the need for runoff dataErrors in water and energy balance closure are influenced more by uncertainties in evaporation rather than precipitationInability of Earth observations to close the water and energy balance of temperate forests

Multivariate calibration using measurements of multiple water balance components has emerged as a potential solution for improving the performance and realism of large scale hydrologic models. In this study we develop a novel multivariate calibration framework to rigorously test whether incorporation of multiple water balance components into calibration can result in sufficiently accurate (behavioral) solutions for all model responses. Unlike previous studies, we use Bayesian calibration to formally define limits of acceptability or error thresholds in order to distinguish behavioral solutions for each of the incorporated fluxes. We apply the framework in the Mississippi river basin for the calibration of a large scale distributed hydrologic model (Noah-MP) with different combinations of model responses - evapotranspiration (ET), soil moisture (SM), and streamflow (SF). The results of the study show that incorporation of additional fluxes and soil moisture (a storage variable) is not always valuable due to significant trade-offs in accuracy among the model responses. In our experiments, only ET and SF could be simulated simultaneously to a reasonable degree of accuracy. In addition, we quantify the trade-offs in accuracy between the model responses using the concept of Pareto optimality. We find that combining ET with other fluxes entails higher trade-offs in accuracy compared to either SM or SF. Unlike deterministic calibration, with the developed framework we are able to identify deficiencies in model parameterization that lead to significant trade-offs in accuracy, especially between ET and SM. We find that the parameters which are insensitive to individual model responses can influence the trade-off relationship between them.

Riparian ecosystems provide various ecosystem services including habitat for a variety of plant and animal communities, biofiltering, and stabilizing stream and river systems. Due to their location, riparian zones often share long borders with agricultural fields where herbicides are commonly applied to eliminate unwanted plants. There is a general concern that exposure of riparian vegetation to off‐target drifted herbicides may adversely impact their health and diversity. We utilized the Normalized Difference Vegetation Index (NDVI) to investigate the long‐term (between 1992 and 2011) trend of riparian vegetation health at 17 locations in the Midwest and Great Plains areas of the United States, where herbicide usage was likely most intense. Assessment of NDVI data demonstrated that long‐term vegetation health did not decline for the studied riparian zones located in proximity to croplands during spring months (April and May). During summer (June and July), while the long‐term vegetation health did not decline for the majority of the sites, there were a few cases in Kansas and Nebraska with a decline in vegetation health (negative‐trending NDVI). Cluster analysis of the negative‐trending NDVI pixels showed that the majority of these pixels were randomly distributed throughout these riparian sites, indicating a lack of shared common causing factors. Similarly, proximity analysis suggested that distance from croplands was not associated with the decline of vegetation health found in these sites, suggesting that exposure to herbicide drift may not be a plausible factor because this would have shown higher impact on pixels closer to the cropland. Changes in canopy coverage and vegetation diversity also did not show any dependence on distance from croplands. Finally, the remote‐sensing–based NDVI data sets used provide only an indirect way of assessing the impact of herbicide drift, and therefore, further work based on field survey data is recommended to completely isolate the impacts of herbicides. Integr Environ Assess Manag 2019;15:528–543. © 2019 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals, Inc. on behalf of Society of Environmental Toxicology & Chemistry (SETAC), Key Points We assessed the riparian vegetation health (VH) of the United States Great Plains for 2 decades.Spring VH remained unchanged or increased at all 17 sites.Summer VH declined at few sites in Kansas and Nebraska.We found no association between decline in VH and distance to croplands.

Unsustainable groundwater withdrawal is a major challenge facing semi-arid and arid regions of the world, where groundwater is the primary source of irrigation water. Conversion of some agricultural land to fallow land is one of the possible solutions to reduce the groundwater withdrawal to bring it to safe yield. Such a solution has a negative economic impact. In this study, we have developed an optimization approach that can use used to identify the size (and type) of the agricultural area to be fallowed to ensure sustainable groundwater use for irrigation with as little economic impact as possible. This approach is based on the concept of profit productivity, which relies on Landsat imageries to calculate crop yield and irrigation water withdrawal, and available in-situ data to calculate energy cost. We have applied this approach to the Neyshabour basin in Iran that has been experiencing unsustainable groundwater withdrawal for the last 30 years. Our sample results indicate that in order to ensure sustainable groundwater withdrawal with minimum economic impact, 4% of the agricultural land needs to be converted to fallow land by fallowing most of the agricultural land used for growing tomatoes.

Author(s): Haleakala, K; Gebremichael, M; Dozier, J; Lettenmaier, DP | Abstract: AbstractSeasonal snow water equivalent (SWE) accumulation in California’s Sierra Nevada is primarily governed by a few orographically enhanced snowstorms. However, as air temperatures gradually rise, resulting in a shift from snow to rain, the governing processes determining SWE accumulation versus ablation become ambiguous. Using a network of 28 snow pillow measurements to represent an elevational and latitudinal gradient across the Sierra Nevada, we identify distributions of critical temperatures and corresponding storm and snowpack properties that describe how SWE accumulation varies across the range at an hourly timescale for water years 2010 through 2019. We also describe antecedent and prevailing conditions governing whether SWE accumulates or ablates during warm storms. Results show that atmospheric moisture regulates a temperature dependence of SWE accumulation. Conditions balancing precipitable water and snow formation requirements produce the most seasonal SWE, which was observed in the (low-elevation) northern and (middle-elevation) central Sierra Nevada. The high southern Sierra Nevada conservatively accumulates SWE with colder, drier air, resulting in less midwinter ablation. These differences explain a tendency for deep, low-density snowpacks to accumulate rather than ablate SWE during warm storms (having median temperatures exceeding 1.0°C), reflecting counteracting liquid storage and internal energy deficits. The storm events themselves in these cases are brief with modest moisture supplies or are otherwise followed immediately by ablation.

Climate warming is expected to reduce radiation-driven snowmelt rates from deep mountain snowpacks, and increase the duration of low-intensity melt periods, especially from shallow snowpacks. It should also increase the humidity-dependence of melt rates, especially in winter. Taken together, these effects will raise the importance of snow ablation in midwinter, the magnitude of which has implications for shifting surface runoff regimes, groundwater recharge, and flood risk. We address two related questions: first, what is the relative effect of radiation, turbulent energy exchange, and precipitation as they relate to midwinter snow ablation, and how important are they to seasonal and annual streamflow generation; and second, how do midwinter ablation episodes and their drivers vary regionally and topographically, and how have those characteristics changed over time (if at all)? We answer these questions using a network of over 500 SnoTel snow pillows across the Western United States that have recorded daily snow water equivalent (SWE) since 1985 or earlier. With ancillary climate data, we identify distributions of midwinter snow ablation episodes along with their trends and sensitivity to meteorological drivers. We then focus on 32 small catchments with nearby daily streamflow and SWE measurements to assess regional differences in the importance of midwinter ablation to winter and annual flows.

This study evaluates the short-to-medium range precipitation forecasts from Global Forecast System for 14 major transboundary river basins in Africa against GPM IMERG “Early”, IMERG “Final”, and CHIRPSv2 products. Daily precipitation forecasts with lead times of 1-day, 5-day, 10-day, and 15-day and accumulated precipitation forecasts with periods of 1-day, 5-day, 10-day, and 15-day are investigated. The 14 selected basins are (1) Senegal; (2) Volta; (3) Niger; (4) Chad; (5) Nile; (6) Awash; (7) Congo; (8) Omo Gibe; (9) Tana; (10) Pangani; (11) Zambezi; (12) Okavango; (13) Limpopo and (14) Orange. For each basin, several sub-basins are defined by the major dams in the basin. Our preliminary results in the Nile river basin show that in terms of temporal variability, there was a good agreement between the forecasted and observed accumulated precipitation on a 15-day basis. When compared to IMERG “Final”, the correlation coefficients of accumulated GFS forecasts scored as high as 0.75. Thus, GFS products provide relatively reliable accumulated precipitation forecasts. However, the precipitation forecasts were mostly biased: they tend to overpredict rainfall for the eastern part of the Nile river, underestimate rainfall for the northern part of the Nile river and produce almost unbiased estimates for the southern part of the river. Additionally, GFS forecasts have a general tendency to underpredict the area of precipitation across the Nile basin. Although the performance of GFS varies at different locations, the GFS precipitation forecasts can be a good reference to dam operators in Africa.

The recent multi-year droughts in California have highlighted the heightened risk of longer and more intense droughts, thus increasing the interest in understanding potential impacts for major economic activities, such as agriculture. This study examines changes in cropping pattern in California’s Central Valley between 2007 and 2016 in response to two consecutive droughts (2007–2009 and 2012–2016), factors driving these changes, and the impact of these changes on groundwater level. Results indicate that Central Valley experienced a shift in cropping pattern from alfalfa, cereals (rice, winter wheat, corn, and oats), and cotton, to nut (almonds, walnuts, and pistachios) and fruit (grapes, oranges, and tomatoes) tree crops. This shift in cropping pattern was likely driven by high crop prices, increasing trend in crop price, and increasing water pumping cost, particularly in the relatively water-stressed southern parts of Central Valley. While the total cropland water use for Central Valley remained the same during 2007–2016 (during both wet and dry years), they vary from county to county. Some counties experienced large reductions in cropland water use, while other counties experienced large increases in cropland water use, indicating the need for county-specific water resource management. The results also indicate that both land management (determining size of fallow land), as well as crop management (choice of crop types), are key factors in water resource management.

BackgroundEthiopia has set a goal to eliminate malaria by 2030; Artemether–lumefantrine (AL) is put as one of the cornerstone strategies for uncomplicatedplasmodium falciparummalaria treatment. However, only focusing on prescribing of the treatment without assessing patients’ adherence could lead to the resistance of the drug. In Ethiopia, there is limited evidence about patients’ adherence to AL and its influencing factors. Therefore, this study aimed at addressing this information gap.MethodsA health facility based cross-sectional study was employed. Participants were selected using simple random sampling technique from registration books of the public health facilities in AsgedeTsimbla. Data were collected from March 24th to April 30th, 2018. We interviewed participants using a pre-tested structured questionnaire, and the blister pack was also inspected at their homes on day 4. Data were entered into Epi-Info and analyzed using SPSS 21. Odds ratios with 95% Confidence Intervals were estimated and the level of significance was declared at p-value ≤ 0.05.ResultsA total of 384 study participants were interviewed with a response rate of 95.5%. The overall AL adherence was 53.6% (95% CI 48.4–58.3%). Children aged ConclusionAL adherence was low. Children aged

The Red Sea is surrounded by a diverse mixture of climates and is spanned by opposite hydrologic end-uses and geopolitical states. Unique water supply management challenges on both sides (related to agricultural and trans-boundary conflict in East Africa, and to groundwater depletion in the Arabian Peninsula) are made more severe by a rising demand, which underscores the importance of understanding shifts in rainfall supply to aid effective action. In this study, we characterize the relative importance of rainfall intensities to annual rainfall, the onset and duration of wet seasons, and the (statistically significant) trends in each of these over the region from 1981 through 2020 using daily gridded (0.05°) precipitation estimates. Results show that heavy rainfall (above 20 mm d−1) does not necessarily benefit annual totals, as the wettest regions are shaped by moderate (between 5 and 20 mm d−1) rainfall coupled with prolonged wet seasons. Observed trends in annual rainfall are underlain by interactions between shifting wet season lengths and rainfall intensities. Wet season length increases for 26% of the region, dampening the inherent drying resulting from shifts toward less-intense rainfall, and bolstering the inherent wetting from shifts toward more-intense rainfall. Regions shifting toward less- (more-)intense rainfall without an expanding wet season generally show negative (insignificant) rainfall trends. This reveals an important control that wet-day frequency has over wet-day intensity alone in shaping annual rainfall changes. We emphasize that the large-scale distribution of these shifts and their regional importance should punctuate cooperative efforts in sustainable resource management and transboundary governance.

Reliable information on medium-range (1–15 day) precipitation forecasts is useful in reservoir operation, among many other applications. Such forecasts are increasingly becoming available from global models. The skills of medium-range precipitation forecasts derived from Global Forecast System (GFS) are assessed in the Senegal River Basin, focusing on the watershed its major hydropower dams: Manantali (located in relatively wet, Southern Sudan climate and mountainous region), Foum Gleita (relatively dry, Sahel climate and low-elevation), and Diama (a large watershed covering almost the entire basin, dominated by Sahel climate). IMERG Final, a satellite product involving rain gauge data for bias correction, is used as reference. GFS has the ability capture the overall spatial and monthly pattern of rainfall in the region. However, GFS tends to overestimate rainfall in the wet parts of the region, and slightly underestimate in the dry part. The skill of daily GFS forecast is low over Manantali (Kling-Gupta Efficiency, KGE of 0.29), but slightly higher over Foum Gleita (KGE of 0.53) and Diama (KGE of 0.59). For 15-day accumulation, GFS forecast shows higher skill over Manantali (KGE of 0.60) and Diama (KGE of 0.79) but does not change much over Foul Gleita (KGE of 0.51) compared to daily rainfall forecasts. IMERG Early, a satellite-only product available at near-real time, has better performance than GFS. This study suggests the need for further improving the accuracy of GFS forecasts, and identifies IMERG Early as a potential source of data that can help in this effort.

Author(s): Alam, Sarfaraz; Gebremichael, Mekonnen; Li, Ruopu; Dozier, Jeff; Lettenmaier, Dennis P

Satellite-based remote sensing offers potential pathways for accurately closing the water and energy balance of watersheds from observations, a fundamental challenge in hydrology. However, previous attempts based on purely satellite-based estimates have been hindered by large data uncertainties and lack of estimates for key components, such as runoff. Here, we use a novel approach based on the Budyko hypothesis to quantify both the degree of closure and its uncertainties in watershed-scale water and energy balance closure arising from an ensemble of 56 global satellite datasets for precipitation (P), terrestrial evaporation (ET), and net radiation (Rn). We use 7 quasi-global precipitation datasets which include CHIRPS, CMORPH, PERSIANN, PERSIANN-CCS, PERSIANN-CDR, TRMM 3B42RT, TRMM 3B43. For ET, we use 8 datasets - AVHRR, SSEBOp, MOD16A3, GLEAM v3.3a, GLEAM v3.3b, CSIRO-PML, BESS, and FluxCom. For Rn, we use the CERES dataset. We find large spatial variability along with aridity, elevation and other gradients. Results show that errors in water and energy balance closure can be attributed primarily to uncertainties in terrestrial evaporation data. These findings have implications for improving the understanding of global hydrology and regional water management and can guide the development of satellite remote sensing datasets and earth system models. In addition, we rank the P and ET datasets that perform the best in closing the combined water and energy balance of global catchments. For P, we see that gauge-calibrated datasets such as PERSIANN-CDR, TRMM 3B43 perform the best. In terms of ET, we see that BESS performs the best in the northern boreal forests and GLEAM performs the best in drylands.

Groundwater plays a critical supporting role in agricultural production in the California Central Valley (CV). Recent prolonged droughts (notably 2007–2009 and 2012–2016) caused dramatic depletion of groundwater, indicating the susceptibility of the CV’s water supply to climate change. To assess the impact of climate change on groundwater storage in the CV, we combined integrated surface water and groundwater models with climate projections from 20 global climate models and thereby explore the vulnerability of CV groundwater under two climate scenarios RCP4.5 and RCP8.5. We found that groundwater has been declining over the past decades (3 km3/year on average during 1950–2009). In the absence of future mitigating measures, this decline will continue, but at a higher rate due to climate change (31% and 39% increase in loss rate under RCP4.5 and RCP8.5). The greatest loss (more than 80% of the total) will occur in the semi-arid southern Tulare region. We performed computational experiments to quantify the relative contribution of future crop water use and headwater inflows to total groundwater storage change. Our results show that, without management changes, continuing declines in future groundwater storage will mainly be attributable to ongoing overuse of groundwater. However, future changes in the seasonality of streamflow into the CV, (small) changes in annual inflows, and increased crop water use in a warmer climate will lead to 40–70% more annual groundwater losses than the current annual average, up to approximately 5 km3/year.

Depletion of groundwater is accelerated due to an increase in water demand for applications in urbanized areas, agriculture sectors, and energy extraction, and dwindling surface water during changing climate. Managed aquifer recharge (MAR) is one of the several methods that can help achieve long-term water sustainability by increasing the natural recharge of groundwater reservoirs with water from non-traditional supplies such as excess surface water, stormwater, and treated wastewater. Despite the multiple benefits of MAR, the wide-scale implementation of MAR is lacking, partly because of challenges to select the location for MAR implementation and identify the MAR type based on site conditions and needs. In this review, we provide an overview of MAR types with a basic framework to select and implement specific MAR at a site based on water availability and quality, land use, source type, soil, and aquifer properties. Our analysis of 1127 MAR projects shows that MAR has been predominantly implemented in sites with sandy clay loam soil (soil group C) and with access to river water for recharge. Spatial analysis reveals that many regions with depleting water storage have opportunities to implement MAR projects. Analyzing data from 34 studies where stormwater was used for recharge, we show that MAR can remove dissolved organic carbon, most metals, E. coli but not efficient at removing most trace organics, and enterococci. Removal efficiency depends on the type of MAR. In the end, we highlight potential challenges for implementing MAR at a site and additional benefits such as minimizing land subsidence, flood risk, augmenting low dry-season flow, and minimizing salt-water intrusion. These results could help identify locations in the water-stressed regions to implement specific MAR for water sustainability.

This study evaluates the accuracy of short-range (1-h to 18-hr lead-time) forecasts from the High-Resolution Rapid Refresh (HRRR) model for five extreme storms in the United States: (1) the September 21–23, 2016, frontal storms in Iowa, (2) the April 28-May 1, 2017, frontal storms in the Southern Midwestern US, (3) the August 25–31, 2017, Hurricane Harvey storms in Texas, (4) the September 13–17, 2018, Hurricane Florence storms in the Carolinas, and (5) the September 4–6, 2019, Hurricane Dorian storms in the Carolinas. The evaluation was carried out by comparison with gauge-corrected Multi-Radar/Multi-Sensor (MRMS-GC) products. In terms of temporal variability, there was a good agreement between the forecasted and observed precipitation on an hourly basis. Thus, the HRRR products provide relatively reliable forecasts. However, the forecasts were mostly biased: they tend to overestimate rainfall for both hurricanes, underestimate rainfall for tropical storms in Iowa, and produce almost unbiased estimates for the frontal storms in Southern Midwestern US. In terms of spatial pattern, the forecasts are able to capture the spatial pattern of hurricanes, however, they produce too many, localized, high-rain intensities for the frontal storms than what the observations show. With regard to the effect of lead times, the 1-h lead forecasts have often lower accuracy than the other lead-time forecasts, while there was no much systematic difference in accuracy among the 2-h to 18-h lead-time forecasts. The bias estimates in the forecast are also examined at different spatial scales, ranging from 2 km × 2 km all the way to 128 km × 128 km. The results show that the bias estimated at smaller spatial scales vary within a large range, mostly within the range of −100% to +100%, indicating that the bias estimates obtained at large scale (hundreds of km grids) are not applicable to bias estimates at small scales, and vice versa. Local-bias correction approaches are therefore preferable over global bias-correction approaches.

Food, energy, and water (FEW) nexus studies require reliable estimates of water availability, use, and demand. In this regard, spatially distributed hydrologic models are widely used to estimate not only streamflow (SF) but also different components of the water balance such as evapotranspiration (ET), soil moisture (SM), and groundwater. For such studies, the traditional calibration approach of using SF observations is inadequate. To address this, we use state-of-the-art global remote sensing-based estimates of ET and SM with a multivariate calibration methodology to improve the applicability of a widely used spatially distributed hydrologic model (Noah-MP) for FEW nexus studies. Specifically, we conduct univariate and multivariate calibration experiments in the Mississippi river basin with ET, SM, and SF to understand the trade-offs in accurately simulating ET, SM, and SF simultaneously. Results from univariate calibration with just SF reveal that increased accuracy in SF at the cost of degrading the spatio-temporal accuracy of ET and SM, which is essential for FEW nexus studies. We show that multivariate calibration helps preserve the accuracy of all the components involved in calibration. The study emphasizes the importance of multiple sources of information, especially from satellite remote sensing, for improving FEW nexus studies.

Routine information on regional evapotranspiration (ET) and dryness index is essential for agricultural water management, drought monitoring, and studies of water cycle and climate. However, this information is not currently available for the East Africa highlands. The main purpose of this study is to develop (1) a new methodology that produces spatially gridded daily ET estimates on a (near) real-time basis exclusively from satellite data, and (2) a new dryness index that depends only on satellite data and weather forecast data. The methodology that calculates daily actual ET involves combining data from two sensors (MODIS and SEVIRI) onboard two kinds of platforms (Terra – polar orbit satellite and MSG – geostationary orbit satellite). The methodology is applied to the East African highlands, and results are compared to eddy covariance measurements at one site. Results show that the methodology produces ET estimates that accurately reproduce the daily fluctuation in ET but tends to underestimate ET on the average. It is concluded that the synergistic use of the polar-orbiting MODIS data and the geostationary-orbiting SEVIRI data has potential to produce reliable daily ET, but further research is needed to improve the accuracy of the results. This study also proposes an operational new dryness index that can be calculated from the satellite-based daily actual ET estimates and daily reference ET estimates based on SEVIRI data and weather forecast air temperature. Comparison of this index against ground measurements of daily actual ET at one site indicates that the new dryness index can be used for drought monitoring.

In the present study, spatial and temporal patterns of rain event properties are analysed. These event properties are rain event depth, event duration, mean event rain rate, peak rain rate and the time span between two consecutive rain events which is referred to as inter-event time (IET). In addition, we assessed how rain event properties change when the period over which rainfall data is aggregated changes from 1 to 6 min and when the minimum inter-event time (MIT) changes from 30 min to 8 h. Rainfall data is obtained from a field campaign in two wet seasons of June–August (JJA) of 2007 and 2008 in Gilgel Abbay watershed that is situated at the source basin of the Upper Blue Nile River in Ethiopia. The rainfall data was automatically recorded at eight stations. The results revealed that rain event depth is more related to peak rain rate than to event duration. At the start and towards the end of the wet season, the rain events have larger depth with longer duration and longer IET than those in mid-season. Event rain rate and IET are strongly related to terrain elevation. Sekela which is on a mountain area has the shortest IET while Bahir Dar which is at the south shore of Lake Tana has the longest IET. The period over which rainfall data is aggregated significantly affected the values of rain event properties that are estimated using relatively small value (30 min) of MIT but its effect diminished when the MIT is increased to 8 h. It is shown that increasing the value of MIT has the largest effect on rain event properties of mountain stations that are characterised by high rainfall intermittency.

BackgroundHepatic venous-occlusive disease is blockage of microscopic veins in the liver causing 20-50% mortality. Ingestion of pyrrolizidine alkaloid plant, radiation therapy, and post-bone-marrow-transplant reactions are the commonest causes. In Ethiopia, a venous-occlusive disease outbreak was identified in 2002 in Tahtay Koraro district, Tigray. Suspected due to ingestion of the toxic pyrrolizidine alkaloid plant Ageratum conyzoids, found throughout the district. We aimed to describe the surveillance data of venous-occlusive disease from September 2006 to August 2016 in Tahtay koraro district, Ethiopia, 2017.MethodologyWe defined a possible Hepatic venous-occlusive disease case as any patient with abdominal pain for at least 2 weeks, abdominal distention, and hepato-splenomegaly during September 2006-August 2016. We reviewed previous district line lists, weekly reports, and clinical records to identify and describe cases. Agricultural interventions were obtained from the agricultural offices of the district.ResultWe identified 179 possible cases with 83 deaths with a case-fatality rate of 46.3%. Among cases, 110 (61.5%) were males and 113 (63%) were >15 years. In total, 164 (91.6%) cases were from one village (Kelakil). The pick number of cases of VOD in this village was during 2008/09 which was 1076. The highest incidence (86/100,000) occurred in 2008. During the study period, 2,746 years of potential life were lost due to Hepatic venous-occlusive disease. Mechanical removal of the Ageratum started in 2011.ConclusionHepatic venous-occlusive disease was an ongoing problem in Tahtay Koraro; However, the problem has largely been alleviated by displacing people from the affected area and removing the causative weed. More research is needed to understand why Kelakil village was more affected despite the widespread presence of the weed. Chemical and mechanical removal of the Ageratum could strengthen intervention activities.

Understanding the spatio-temporal characteristics of water storage changes is crucial for Ethiopia, a country that is facing a range of challenges in water management caused by anthropogenic impacts as well as climate variability. In addition to this, the scarcity of in situ measurements of soil moisture and groundwater, combined with intrinsic “scale limitations” of traditional methods used in hydrological characterization are further limiting the ability to assess water resource distribution in the region. The primary objective of this study is therefore to apply remotely sensed and model data over Ethiopia in order to (i) test the performance of models and remotely sensed data in modeling water resources distribution in un-gauged arid regions of Ethiopia, (ii) analyze the inter-annual and seasonal variability as well as changes in total water storage (TWS) over Ethiopia, (iii) understand the relationship between TWS changes, rainfall, and soil moisture anomalies over the study region, and (iv) identify the relationship between the characteristics of aquifers and TWS changes. The data used in this study includes; monthly gravity field data from the Gravity Recovery And Climate Experiment (GRACE) mission, rainfall data from the Tropical Rainfall Measuring Mission (TRMM), and soil moisture from the Global Land Data Assimilation System (GLDAS) model. Our investigation covers a period of 8 years from 2003 to 2011. The results of the study show that the western part and the north-eastern lowlands of Ethiopia experienced decrease in TWS water between 2003–2011, whereas all the other regions gained water during the study period. The impact of rainfall seasonality was also seen in the TWS changes. Applying the statistical method of Principal Component Analysis (PCA) to TWS, soil moisture and rainfall variations indentified the dominant annual water variability in the western, north-western, northern, and central regions, and the dominant seasonal variability in the western, north-western, and the eastern regions. A correlation analysis between TWS and rainfall indicated a minimum time lag of zero to a maximum of six months, whereas no lag is noticeable between soil moisture anomalies and TWS changes. The delay response and correlation coefficient between rainfall and TWS appears to be related to recharge mechanisms, revealing that most regions of Ethiopia receive indirect recharge. Our results also show that the magnitude of TWS changes is higher in the western region and lower in the north-eastern region, and that the elevation influences soil moisture as well as TWS.

The demand for accurate satellite rainfall products is increasing particularly in Africa where ground-based data are mostly unavailable, timely inaccessible, and unreliable. In this study, the accuracy of three widely used, near-global, high-resolution satellite rainfall products (CMORPH, TMPA-RT v7, TMPA-RP v7), with a spatial resolution of 0.25° and a temporal resolution of 3 h, is assessed over the Blue Nile River Basin, a basin characterized by complex terrain and tropical monsoon. The assessment is made using relatively dense experimental networks of rain gauges deployed at two, 0.25° × 0.25°, sites that represent contrasting topographic features: lowland plain (mean elevation of 719 m.a.s.l.) and highland mountain (mean elevation of 2268 m.a.s.l.). The investigation period covers the summer seasons of 2012 and 2013. Compared to the highland mountain site, the lowland plain site exhibits marked extremes of rain intensity, higher mean rain intensity when it rains, lower frequency of rain occurrence, and smaller seasonal rainfall accumulation. All the satellite products considered tend to overestimate the mean rainfall rate at the lowland plain site, but underestimate it at the highland mountain site. The satellite products miss more rainfall at the highland mountain site than at the lowland plain site, and underestimate the heavy rain rates at both sites. Both sites have uncertainty (root mean square error) values greater than 100% for 3 h accumulations of

Motivated by the field observations of fall velocity and axis ratio deviations from predicted terminal velocity and equilibrium axis ratio values, the combined effects of raindrop fall velocity and axis ratio deviations on dual-polarization radar rainfall estimations were investigated. A radar rainfall retrieval algorithm [Colorado State University–Hydrometeor Identification Rainfall Optimization (CSU-HIDRO)] served as the test bed. Subsequent investigations determined that the available field measurements, which were very limited in scope, of the fall velocity and axis ratio deviations indicated rain-rate estimation errors of approximately 20%. Based on these findings, a sensitivity study was then performed using uncorrelated fall velocity and axis ratio deviations around the predicted values. Significant rain-rate estimation errors were observed for the realistic combinations of fall velocity and axis ratio deviations. It was shown that the maximum rain-rate estimation error can reach up to approximately 200% for combinations of fall velocity and axis ratio deviations (5000 drop size distribution samples were simulated for each combination) between −10% and +10% of the predicted values for each. The maximum standard deviation of errors was as great as 75% for the same combinations of fall velocity and axis ratio deviations. The authors found that use of dual-polarization radars to accurately estimate rainfall, during natural rain events, also requires a reanalysis of the parameterizations for raindrop fall velocity and axis ratio. These parameterizations should consider both the coupling between these two parameters and factors that may introduce any possible deviations of the predicted values of these parameters.

High-resolution satellite precipitation estimates, such as the Climate Prediction Center morphing technique (CMORPH), provide alternative sources of precipitation data for hydrological applications, especially in regions where adequate ground-based instruments are unavailable. These estimates are, however, subject to large errors, especially at times of heavy precipitation. This paper presents a method to distributionally convert a set of CMORPH estimates into ground-based Next Generation Weather Radar (NEXRAD) estimates. As our concern lies with floods and extreme precipitation events, a peaks-over-threshold extreme value approach is adopted that fits a generalized Pareto distribution to the large precipitation estimates. A quantile matching transformation is then used to convert CMORPH values into NEXRAD values. The methods are applied in the analysis of 6 yr of precipitation observations from 625 pixels centered around eastern Oklahoma.

An integrated assessment of crop-energy-water (CEW) nexus is critical to understand the tradeoffs and synergies for better management of sustainable agricultural systems. In this study, we evaluate the historic evolution of CEW interactions in the Central Valley, California, a critical agricultural region that produces approximately 50% of US fruits, nuts and vegetables. Specifically, we consider three nexus elements, including water use for irrigation (blue water), energy use for groundwater pumping, and crop yield (for all crops aggregated, almond and cotton). To quantify the interactions between CEW elements, we estimate the water use for cropping (water footprint) and energy use for cropping (energy footprint). We conduct the analyses for four historical periods, i.e., 2007&ndash, 2009 (Drought 1), 2010&ndash, 2011 (Post-drought 1), 2012&ndash, 2015 (Drought 2) and 2016&ndash, 2018 (Post-drought 2). We find that the southern regions (San Joaquin and Tulare) are susceptible to greater stress on energy and water, especially during droughts. The groundwater footprint (GWF) has been continuously increasing due to greater crop water use and a shift from row crops to profitable water-intensive tree crops. The GWF in Tulare during Drought 2 was around 60% higher than Drought 1, where the GWF in Tulare was almost twice that of Sacramento. The energy and water uses for almond production have increased during the recent periods, whereas their uses have mostly decreased for cotton. On average, energy and water footprints under almond crop scenario are around 3&ndash, 3.5 times as much as the footprints under all crops scenario.

A simple remote sensing evapotranspiration (ET) model (Sim-ReSET) has been proposed but only tested using field measurements at a site with a semi-arid climate. Its performance for mapping ET using only satellite data remained unknown. In this study, the Sim-ReSET model was further evaluated for ET estimation driven by only MODIS data products. The estimated ET rates were compared with ground-based observational data from a variety of ecosystems and climates across China. The results show that MODIS-based ET estimates are consistent with both the ET measurements from eddy covariance flux towers and those from the Penman-Monteith method combined with micrometeorological data. Evaporation fraction (EF) is indicative of land surface moisture. The derivative EF maps demonstrate that the proposed ET data set obtained from the Sim-ReSET model and MODIS data is capable of capturing the spatio-temporal pattern of land surface moisture for different land covers with different climates. Editor Z.W. Kundzew...

article i nfo In this work we demonstrate the utility of satellite remote sensing for river discharge nowcasting and forecasting for two major rivers, the Ganges and Brahmaputra, in southern Asia. Passive microwave sensing of the river and floodplain at more than twenty locations upstream of Hardinge Bridge (Ganges) and Bahadurabad (Brahmaputra) gauging stations are used to: 1) examine the capability of remotely sensed flow information to track the down- stream propagation of river flow waves and 2) evaluate their use in producing river flow nowcasts, and forecasts at 1-15 days lead time. The pattern of correlation between upstream satellite data and in situ observations of downstream discharge is used to estimate wave propagation time. This pattern of correlation is combined with a cross-validation method to select the satellite sites that produce the most accurate river discharge estimates in a lagged regression model. The results show that the well-correlated satellite-derived flow (SDF) signals were able to detect the propagation of a river flow wave along both river channels. The daily river discharge (contem- poraneous) nowcast produced from the upstream SDFs could be used to provide missing data estimates given its Nash-Sutcliffe coefficient of 0.8 for both rivers; and forecasts have considerably better skill than autoregressive moving-average (ARMA) model beyond 3-day lead time for Brahmaputra. Due to the expected better accuracy of the SDF for detecting large flows, the forecast error is found to be lower for high flows compared to low flows. Overall, we conclude that satellite-based flow estimates are a useful source of dynamical surface water information in data-scarce regions and that they could be used for model calibration and data assimilation purposes in

Summary In this paper, a two-level self-organizing map (SOM) clustering technique was used to identify spatially homogeneous clusters of precipitation satellite data, and to choose the most operative and effective data for a feed-forward neural network (FFNN) to model rainfall–runoff process on a daily and multi-step ahead time scale. The wavelet transform (WT) was also used to extract dynamic and multi-scale features of the non-stationary runoff time series and to remove noise. The performance of the coupled SOM–FFNN model was compared to the newly proposed combined SOM–WT–FFNN model. The performance of these two models was also compared to that of a conventional forecasting method, namely the auto regressive integrated moving average with exogenous input (ARIMAX) model. Daily precipitation data from two satellites and four rain gauges, as well as runoff values recorded from January 2003 to December 2007 in the Gilgel Abay watershed in Ethiopia were used to calibrate and validate the models. Runoff predictions via all of the above methods were investigated for both single-step-ahead and multi-step-ahead lead times. The results indicated that the use of spatial and temporal pre-processed data in the FFNN model led to a promising improvement in its performance for rainfall–runoff forecasting. In the validation phase of single and multi-step-ahead forecasting, it was determined that the SOM–WT–FFNN models provide more accurate forecasts than the SOM–FFNN models (the determination coefficients for validation of the SOM–FFNN and SOM–WT–FFNN models were 0.80 and 0.93, respectively). The proposed FFNN model coupled with the SOM clustering method decreased the dimensionality of the input variables and consequently the complexity of the FFNN models. On the other hand, the application of the wavelet transform to the runoff data increased the performance of the FFNN rainfall–runoff models in predicting runoff peak values by removing noise and revealing the dominant periods.

Remote sensing datasets are increasingly being used to provide spatially explicit large scale evapotranspiration (ET) estimates. The focus of this study was to estimate and thematically map pixel-by-pixel basis, and compare the actual evapotranspiration (ETa) of the Wonji Shoa Sugarcane Estate using Surface Energy Balance Algorithm for Land (SEBAL), Simplified Surface Energy Balance (SSEB) and Operational Simplified Surface Energy Balance (SSEBop) algorithms on Landsat7 ETM+ images acquired on four days in 2002. The algorithms were based on image processing which uses spatially distributed spectral satellite data and ground meteorological data to derive the surface energy balance components. The results obtained revealed that the ranges of the daily ETa estimated on January 25, February 26, September 06 and October 08, 2002 using SEBAL were 0.0–6.85, 0.0–9.36, 0.0–3.61, 0.0–6.83 mm/day; using SSEB 0.0–6.78, 0.0–7.81, 0.0–3.65, 0.0–6.46 mm/day, and SSEBop were 0.05–8.25, 0.0–8.82, 0.2–4.0, 0.0–7.40 mm/day, respectively. The Root Mean Square Error (RMSE) values between SSEB and SEBAL, SSEBop and SEBAL, and SSEB and SSEBop were 0.548, 0.548, and 0.99 for January 25, 2002; 0.739, 0.753, and 0.994 for February 26, 2002;0.847, 0.846, and 0.999 for September 06, 2002; 0.573, 0.573, and 1.00 for October 08, 2002, respectively. The standard deviation of ETa over the sugarcane estate showed high spatio-temporal variability perhaps due to soil moisture variability and surface cover. The three algorithm results showed that well watered sugarcane fields in the mid-season growing stage of the crop and water storage areas had higher ETa values compared with the other dry agricultural fields confirming that they consumptively use more water. Generally during the dry season ETa is limited to water surplus areas only and in wet season, ETa was high throughout the entire sugarcane estate. The evaporation fraction (ETrF) results also followed the same pattern as the daily ETa over the sugarcane estate. The total crop and irrigation water requirement and effective rainfall estimated using the Cropwat model were 2468.8, 2061.6 and 423.8 mm/yr for January 2001 planted and 2281.9, 1851.0 and 437.8 mm/yr for March 2001 planted sugarcanes, respectively. The mean annual ETa estimated for the whole estate were 107 Mm3, 140 Mm3, and 178 Mm3 using SEBAL, SSEB, and SSEBop, respectively. Even though the algorithms should be validated through field observation, they have potential to be used for effective estimation of ET in the sugarcane estate.

Most existing remote sensing-based evapotranspiration (ET) algorithms rely exclusively on polar-orbiting satellites with thermal infrared sensors, and therefore the resulting ET values represent only "instantaneous or snapshot" values. However, daily ET is more meaningful and useful in applications. In this study, daily ET estimates are obtained by combining data from the MODIS sensor aboard the polar-orbiting Terra satellite and the SEVIRI sensor aboard the geostationary-orbiting MSG satellite. The procedure consists of estimating the instantaneous evaporative fraction (EF) based on the MODIS/Terra land data products, and estimating the daily net radiation and daily available energy based on the 30-min SEVIRI/MSG data products. Assuming constant EF during the daytime, daily ET is estimated as the product of the SEVIRI/MSG-based daily available energy and MODIS/Terra-based instantaneous EF. The daily ET estimates are evaluated against flux tower measurements at four validation sites in Africa. Results indicate that the synergistic use of SEVIRI/MSG and MODIS/Terra has the potential to provide reliable estimates of daily ET during wet periods when daily ET exceeds 1. mm/day. The satellite-based daily ET estimates however tend to underestimate ET by 13% to 35%. The daily ET estimation algorithm can further be improved by incorporating a temporal data-filling interpolation technique to estimate the unavailable net radiation information during cloudy sky conditions, and by improving the accuracy of the instantaneous EF. The assumption of constant evaporative fraction during the daytime is reasonable, and does not result in substantial errors in the daily ET estimates.

Net radiation is a key component of the energy balance, whose estimation accuracy has an impact on energy flux estimates from satellite data. In typical remote sensing evapotranspiration (ET) algorithms, the outgoing shortwave and longwave components of net radiation are obtained from remote sensing data, while the incoming shortwave (RS ↓) and longwave (RL ↓) components are typically estimated from weather data using empirical equations. This study evaluates the accuracy of empirical equations commonly used in remote sensing ET algorithms for estimating RS ↓ and RL ↓ radiation. Evaluation is carried out through comparison of estimates and observations at five sites that represent different climatic regions from humid to arid. Results reveal (1) both RS ↓ and RL ↓ estimates from all evaluated equations well correlate with observations (R2 ≥ 0.92), (2) RS ↓ estimating equations tend to overestimate, especially at higher values, (3) RL ↓ estimating equations tend to give more biased values in arid and semi-arid regions, (4) a model that parameterizes the diffuse component of radiation using two clearness indices and a simple model that assumes a linear increase of atmospheric transmissivity with elevation give better RS ↓ estimates, and (5) mean relative absolute errors in the net radiation (Rn) estimates caused by the use of RS ↓ and RL ↓ estimating equations varies from 10% to 22%. This study suggests that Rn estimates using recommended incoming radiation estimating equations could improve ET estimates. © 2013 by the authors.

The correct forecasting of unrecorded data could be enormously helpful in designing water projects and preventing related damages. The conventional methods available for rainfall estimation usually take a long time to estimate the missing data, and their estimations may have many errors in the long-term simulations. In this study, the capabilities of different Artificial Neural Networks (ANNs) were analyzed in estimating missing data from the Ardabel plain rain gauge stations located in northwestern Iran. Accordingly, six different structures of ANNs were used, and their efficiencies in terms of the mean squared error, training, and validation determination coefficients to select better-estimated missing data were examined. The results revealed that the best model is composed of the feed-forward networks, trained by the Levenberg-Marquardt algorithm and considering only one hidden layer. For each of the stations with a complete data set, an ANN was trained. Data gaps from other stations were obtained by the proposed ANN models. Furthermore, an integrated ANN was developed to investigate the hidden spatial relationships among the rainfall data of the stations as well as temporal auto-correlations. The results indicated the superiority of the proposed integrated model. After the estimation of the rain data gaps, the K-means clustering method was also employed as a data pre-processing method to improve the accuracy of the estimation, and the method led to better results.

This study focuses on evaluating four widely used global high-resolution satellite rainfall products [the Climate Prediction Center’s morphing technique (CMORPH) product, the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) near-real-time product (3B42RT), the TMPA method post-real-time research version product (3B42), and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) product] with a spatial resolution of 0.25° and temporal resolution of 3 h through their streamflow simulations in the Soil and Water Assessment Tool (SWAT) hydrologic model of a 299-km2 mountainous watershed in Ethiopia. Results show significant biases in the satellite rainfall estimates. The 3B42RT and CMORPH products perform better than the 3B42 and PERSIANN. The predictive ability of each of the satellite rainfall was examined using a SWAT model calibrated in two different approaches: with rain gauge rainfall as input, and with each of the satellite rainfall products as input. Significant improvements in model streamflow simulations are obtained when the model is calibrated with input-specific rainfall data than with rain gauge data. Calibrating SWAT with satellite rainfall estimates results in curve number values that are by far higher than the standard tabulated values, and therefore caution must be exercised when using standard tabulated parameter values with satellite rainfall inputs. The study also reveals that bias correction of satellite rainfall estimates significantly improves the model simulations. The best-performing model simulations based on satellite rainfall inputs are obtained after bias correction and model recalibration.

Two conditions are required when a remotely sensed vegetation index–land surface temperature VI–Ts diagram is used to estimate the land surface moisture index LSMI and air temperature Ta. First, a suitable sampling window size is required to define an ideal VI–Ts diagram. Second, Ta must be homogeneous across the sampling window. In this study, the Shannon diversity index SDI and the semivariogram method were used to evaluate the VI–Ts diagram for estimating LSMI and Ta from Advanced Spaceborne Thermal Emission Reflection Radiometer ASTER and MODerate-resolution Imaging Spectroradiometer MODIS datasets. The results show that Ta is homogeneous across a sampling window with a width of several tens of kilometres 46.0–83.6 km based on the semivariogram method and spatial autocorrelation analysis of the Ta from 83 meteorological stations in the North China Plain NCP in 2003. When the SDIs of VI and Ts are respectively larger than 77% and 63% of their maximums within predetermined sampling windows, LSMI estimations by ASTER and Ta estimations by ASTER and MODIS are reliable.

Summary Objective To assess the suitability of satellite temperature and precipitation datasets for investigating the dependence of Schistosoma mansoni disease transmission on meteorological conditions in an irrigated agricultural region in Ethiopia. Methods Data used were monthly number of patients infected with S. mansoni and seeking treatment at the local hospital, monthly maximum air temperature from a local weather station, monthly average land surface temperature from MODIS satellite data, monthly total precipitation from a local rain gauge and precipitation estimates from four widely used satellite products, namely, TMPA 3B42RT, TMPA 3B42, CMORPH and PERSIANN. The number of patients was used as proxy for vector abundance. Results Temperature and precipitation play a role in the transmission of S. mansoni disease. There is a weak but significant positive correlation between monthly maximum air temperature derived from a meteorological station (or average land surface temperature derived from MODIS satellite product) and the number of patients in the same month. There is a significant negative correlation between monthly precipitation volume (derived from rain gauge or satellite data) and number of patients at lags of 1 and 2 months. Conclusion Satellite temperature and precipitation products provide useful information to understand and infer the relationship between meteorological conditions and S. mansoni prevalence. Objetivo: Evaluar la idoneidad de los conjuntos de datos satelitales de temperatura y precipitacion para investigar la dependencia de la transmision de la enfermedad por S. mansoni de las condiciones metereologicas en una region agricola e irrigada de Etiopia. Metodos: Los datos utilizados eran el numero mensual de pacientes infectados con S. mansoni que buscaban tratamiento en el hospital local; la temperatura maxima del aire tomada de una estacion metereologica local; la temperatura media mensual de la superficie de datos del sistema satelital MODIS; la precipitacion mensual total de un pluviometro local y la precipitacion estimada a partir de cuatro productos satelitales de amplio uso: TMPA 3B42RT, TMPA 3B42, CMORPH y PERSIANN. El numero de pacientes se utilizo como un proxy de abundancia del vector. Resultados: La temperatura y la precipitacion juegan un papel importante en la transmision de la enfermedad por S. mansoni. Hay una correlacion debil pero significativa entre la temperatura maxima del aire derivada de una estacion metereologica (o la temperatura promedio de la superficie terrestre derivado del producto satelital MODIS) y el numero de pacientes en el mismo mes. Hay una correlacion negativa significativa entre el volumen de precipitacion mensual (derivado del pluviometro o datos satelitales) y el numero de pacientes en intervalos de uno o dos meses. Conclusion: Los productos de temperatura y precipitacion por satelite dan una informacion util para entender e inferir la relacion entre las condiciones metereologicas y la prevalencia de S. mansoni.

The objective is to assess the use of the Climate Prediction Center morphing method (CMORPH) (~0.073° latitude–longitude, 30 min resolution) rainfall product as input to the physics-based fully distributed Gridded Surface–Subsurface Hydrologic Analysis (GSSHA) model for streamflow simulation in the small (21.4 km2) Hortonian watershed of the Goodwin Creek experimental watershed located in northern Mississippi. Calibration is performed in two different ways: using rainfall data from a dense network of 30 gauges as input, and using CMORPH rainfall data as input. The study period covers 4 years, during which there were 24 events, each with peak flow rate higher than 0.5 m3 s−1. Streamflow simulations using CMORPH rainfall are compared against observed streamflows and streamflow simulations using rainfall from a dense rain gauge network. Results show that the CMORPH simulations captured all 24 events. The CMORPH simulations have comparable performance with gauge simulations, which is striking given the significant differences in the spatial scale between the rain gauge network and CMORPH. This study concludes that CMORPH rainfall products have potential value for streamflow simulation in such small watersheds. Overall, the performance of CMORPH-driven simulations increases when the model is calibrated with CMORPH data than when the model is calibrated with rain gauge data.

High resolution satellite-based rainfall estimates (SREs) have enormous potential for use in hydrological applications, particularly in the developing world as an alternative to conventional rain gauges which are typically sparse. In this study, three SREs have been evaluated against collocated rain gauge measurements in Ethiopia across six river basins that represent different rainfall regimes and topography. The comparison is made using five-year (2003–2007) averages, and results are stratified by river basin, elevation and season. The SREs considered are: the Climate Prediction Center morphing method (CMORPH), Precipitation Estimation from Remotely Sensed Information Using Neural Networks (PERSIANN) and the real-time version of the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42RT. Overall, the microwave-based products TMPA 3B42RT and CMORPH outperform the infrared-based product PERSIANN: PERSIANN tends to underestimate rainfall by 43 %, while CMORPH tends to underestimate by 11 % and TMPA 3B42RT tends to overestimate by 5 %. The bias in the satellite rainfall estimates depends on the rainfall regime, and, in some regimes, the elevation. In the northwest region, which is characterized mainly by highland topography, a humid climate and a strong Intertropical Convergence Zone (ITCZ) effect, elevation has a strong influence on the accuracy of the SREs: TMPA 3B42RT and CMORPH tend to overestimate at low elevations but give reasonably accurate results at high elevations, whereas PERSIANN gives reasonably accurate values at low elevations but underestimates at high elevations. In the southeast region, which is characterized mainly by lowland topography, a semi-arid climate and southerly winds, elevation does not have a significant influence on the accuracy of the SREs, and all the SREs underestimate rainfall across almost all elevations.

The objective is to assess the suitability of commonly used high-resolution satellite rainfall products (CMORPH, TMPA 3B42RT, TMPA 3B42 and PERSIANN) as input to the semi-distributed hydrological model SWAT for daily streamflow simulation in two watersheds (Koga at 299 km2 and Gilgel Abay at 1656 km2) of the Ethiopian highlands. First, the model is calibrated for each watershed with respect to each rainfall product input for the period 2003–2004. Then daily streamflow simulations for the validation period 2006–2007 are made from SWAT using rainfall input from each source and corresponding model parameters; comparison of the simulations to the observed streamflow at the outlet of each watershed forms the basis for the conclusions of this study. Results reveal that the utility of satellite rainfall products as input to SWAT for daily streamflow simulation strongly depends on the product type. The 3B42RT and CMORPH simulations show consistent and modest skills in their simulations but underestimate the large flood peaks, while the 3B42 and PERSIANN simulations have inconsistent performance with poor or no skills. Not only are the microwave-based algorithms (3B42RT, CMORPH) better than the infrared-based algorithm (PERSIANN), but the infrared-based algorithm PERSIANN also has poor or no skills for streamflow simulations. The satellite-only product (3B42RT) performs much better than the satellite-gauge product (3B42), indicating that the algorithm used to incorporate rain gauge information with the goal of improving the accuracy of the satellite rainfall products is actually making the products worse, pointing to problems in the algorithm. The effect of watershed area on the suitability of satellite rainfall products for streamflow simulation also depends on the rainfall product. Increasing the watershed area from 299 km2 to 1656 km2 improves the simulations obtained from the 3B42RT and CMORPH (i.e. products that are more reliable and consistent) rainfall inputs while it deteriorates the simulations obtained from the 3B42 and PERSIANN (i.e. products that are unstable and inconsistent) rainfall inputs.